he Monetary Authority of Singapore (MAS) and The Association of Banks in Singapore (ABS) today awarded 12 FinTech companies a total of SG$1.2 million divided for 12 different companies at the Fintech Awards, which took place at the third Singapore FinTech Festival.
This time around, the awards featured a greater ASEAN representation, with a focus on financial inclusion, spanning different business areas like credit-scoring, mobile security, anti-money laundering, and digital investment. The Fintech Awards, supported by PwC, recognises innovative FinTech solutions that have been implemented by FinTech companies, financial institutions and technology companies.
This year, 40 finalists were shortlisted from more than 280 global submissions including the companies who participated in the ASEAN PitchFest6. The winners were selected by a panel of 17 judges who represent a cross-section of international and local experts from the private and public sectors. The entries were evaluated based on four criteria: impact, practicality, interoperability, and uniqueness and creativity.
The panel of judges includes representatives from Accenture Technology, Allianz, AMTD Group, Credit Ease, DBS, Deloitte, GIC, Grammen Foundation India, HSBC, Insignia Venture Partners, Jungle Ventures, Mastercard, The Boston Consulting Group, The Disruptive Group, True Global Ventures, UOB and Vertex Ventures.
ASEAN Open Award Top 3
First Place: LenddoEFL (Philippines)
The company wants to provide people access to powerful financial products without exorbitant costs, quickly and more conveniently by using AI and advanced analytics to bring together digital and behavioural data. This helps lenders serve the underbanked. LenddoEFL has provided credit scoring, verification and insights to 50+ financial institutions, serving over 7 million people.
Singapore, 14 November 2018… The Monetary Authority of Singapore (MAS) and The Association of Banks in Singapore (ABS) today awarded 12 FinTech companies a total of S$1.2 million at the FinTech Awards, which took place at the third Singapore FinTech Festival.
ASEAN Open Award
1st place – LenddoEFL (Philippines)
2nd place – SQREEM Technologies (Singapore)
3rd place – Finantix Asia Pacific (Singapore)
ASEAN SME Award
1st place – FinAccel Teknologi (Indonesia)
2nd place – Katipult (Thailand)
3rd place – MoneyMatch Transfer (Malaysia)
Singapore Founder Award
1st place – CCRManager
2nd place – Cynopsis Solutions
3rd place – Thin Margin
1st place – Everspin (South Korea)
2nd place – Naffa Innovations (India)
3rd place – Keychain (Japan)
Posted on APAC CIO Outlook website. Refer to this link to read full article.
by Howard Lince III, Director of Enginerring, LenddoEFL
At LenddoEFL, we work at the intersection of big data, machine learning, and financial inclusion in emerging markets. Each of these imply a level of server sophistication that would be cripplingly difficult without Amazon Web Services (AWS). Our mission is to provide one billion people access to powerful financial products at a lower cost, faster and more conveniently. We use AI and advanced analytics to bring together the best sources of digital and behavioral data to help lenders in emerging markets confidently serve underbanked people and small businesses. To date, we have provided credit scoring, verification and insights products to 50+ financial institutions, serving seven million people. We’ve been able to manage all of this with a team of three infrastructure engineers managing 300+ servers. Read full article.
By: Jonathan Winkle, Manager of Behavioral Sciences, LenddoEFL
The last post showed how our psychometric content reveals people’s personality traits, but our assessment also captures an abundance of metadata. Metadata is information about how people process the questions and exercises they complete. Here are some examples.
How long did an applicant take to answer a question compared to their average response time?
How many times did an applicant change their mind and switch their response before submitting their answer?
Is the applicant’s information consistent with their written request to the financial institution? (e.g., requested loan amount)
By measuring metadata, LenddoEFL’s approach goes beyond what is possible in traditional credit applications to reveal more information about applicants. Consider the following question from our test:
For this question, we consider how long it took the applicant to slide to one answer or another and whether they changed their opinions in the middle. Someone who is confident that they are an organized person should move the slider in only one direction and relatively quickly. Quick, smooth answers belie confidence, whereas slow, wavering responses demonstrate uncertainty.
The relationship between response time and default rate can be complex. Consider another psychometric exercise:
In this case response time was a non-linear predictor of default, where both slow and fast response times were associated with a greater credit risk!
There are many ways to interpret response time metadata. If an applicant answers a question quickly, are they confident or are they cheating? If they are taking a long time to respond, are they having difficulty understanding the question or putting extra effort into getting their answer right? By collecting metadata across all questions, we can compare a single response time to the applicant’s overall response time distribution to differentiate things like confidence and cheating (see graph below).
Metadata reveals another layer of behavior on top of the personality traits we target and can be used to identify features such as confidence, cheating, and confusion. These behavioral traits can be used for predicting default and ensuring that we are collecting high quality data for our models.
By: Jonathan Winkle, Manager of Behavioral Sciences, LenddoEFL
At LenddoEFL, we collect various forms of alternative data to help lenders verify identities, analyze credit risk, and better understand an individual. One of our most important tools for financial inclusion is our psychometric assessment. While some people still lack a robust digital footprint, everyone has a psychological profile that can be characterized and used for alternative credit scoring.
In this series of posts, we shed light on the science behind the LenddoEFL psychometric assessment and how we’ve pioneered an approach to measure anyone’s creditworthiness.
Psychometrics for credit assessment
LenddoEFL employs a global research team to ensure our assessment captures the most important personality traits that predict default. We deliver innovative psychometric content by combining insights from leading academics with years of in-house research and development.
Each question in our assessment is targeted to reveal psychological attributes related to creditworthiness. We quantify behaviors and attitudes such as individual outlook, self-confidence, conscientiousness, integrity, and financial decision-making in order to build an applicant’s psychometric profile. By comparing this profile to others in the applicant pool, we can better understand and predict an individual’s likelihood of default.
Psychometric example content: Financial Impulsivity
The marshmallow test asks children whether they would you like one marshmallow now or two marshmallows later, and since its advent, psychologists have recognized that the ability to delay rewards is an important predictor of later success in life.
While adults might not long for marshmallows the same way children do, a similar test can be performed using financial rewards, and research shows that people who are better at delaying rewards are less likely to default on their loans.
Drawing from this research, we ask applicants which of two options they would prefer, a smaller sooner amount of money, or a larger later amount (see image below). Asking people for their preferences across a range of monetary values and temporal delays reveals a quantitative profile of their financial impulsivity, which is indicative of their likelihood to repay debts (If you’re curious about how we deal with people trying to cheat or game the assessment, please see this blog post on our Score Confidence algorithm).
Psychometric example content: Locus of Control
When times get tough, some people believe they can take action to overcome hardships while others believe that the challenges they face are altogether out of their hands. Those who believe their lives are governed by outside forces, an external Locus of Control, are more risk-averse and have more difficulty managing their credit.
We ask applicants to rate their agreement with a battery of statements measuring their Locus of Control, such as “My life is mostly controlled by chance events,” and “It is mostly up to luck whether or not I have many friends.” By asking these types of questions, we can precisely quantify someone’s Locus of Control along a spectrum of internal-to-external and use this data to predict default.
LenddoEFL delivers an innovative psychometric assessment by combining evidence from academia with active, internal research and development. The examples above demonstrate how we quantify certain personality traits, and the myriad exercises we use in the field allow us to produce a rich psychological profile that is predictive of credit risk. In the next post we will explore the concept of metadata, which will show that how people answer psychometric questions is just as important as the answers themselves.
Aligned with Philippine banking regulation, technology-aided verification from LenddoEFL can help more people get faster, more convenient access to financial services.
MANILA, PHILIPPINES (PRWEB) AUGUST 28, 2018
LenddoEFL, whose verification, credit scoring, and consumer insights helps leading banks make data-driven decisions, launched an electronic Know Your Customer (eKYC) product for customers applying for credit card and bank accounts at Philippine financial institutions consistent with Bangko Sentral ng Pilipinas (BSP) regulation.
To date, Know Your Customer (KYC) regulations in the Philippines have always required a face-to-face or real-time online interview to onboard new-to-card or new-to-bank current account/savings account (CASA) customers.
Now, customers will be able to get verified as part of a CASA or credit card account application faster and more conveniently by opting-in to avail of the eKYC solution from their mobile phone. LenddoEFL’s eKYC solution offers a simpler way for banks to onboard new customers. See BSP Circular 950, Subsection X806.2 item D for details on using information and communication technology (ICT) in the conduct of face to face contact.
“This is a game-changer as we continue to adopt alternative digital verification and scoring to help push for a more secure, faster and reliable verification process to onboard more unbanked and underserved segments into the financial system, supporting BSP's mission of financial inclusion,” said Judith Dumapay, APAC Sales Director Philippines, LenddoEFL.
Each bank considering to use technology-aided verification must do so within their risk-based customer acceptance policies and anchored on the results of their risk assessment process. They also remain responsible for watchlist screening.
I have been rolling out alternative data initiatives for financial inclusion across Latin America for several years. At some point, my clients ask: “is this going to work?” My usual answer is “I’ve failed enough times to have figured this out.”
This is a fairly new and not completely mature field. LenddoEFL has been doing this for over 10 years. While there is still a lot to learn, my team and I can share some wisdom.
In response to Accelerating Financial Inclusion with New Data, I recently wrote about the promise and challenge of using alternative data to bank the unbanked. We’ve learned a lot about applying alternative data and have identified five key success factors:
1. Aim at the pain
2. Build on top of your current business
3. Leverage the best data source for you
4. Partner with somebody that can handle multiple data sources
5. Persevere. Capture low-hanging fruit without losing sight of the big prize
We will tackle one at a time.
1. Aim at the pain
Some financial institutions come to us interested in “trying out” alternative data. Our usual question is “what problem are you trying to solve?” Sometimes they are not clear about what they want to solve, and sometimes they want to fix too many things at the same time. The whole approach for the initiative will depend on this understanding. Choose one pain, focus on it, and build the KPIs to measure success according to this.
Keep repeating to everybody the pain you are attempting to solve to make sure everybody shares the same understanding.
These are some examples from our experience:
• An MFI wanted to increase productivity per loan officer while maintaining default rates: reduce turn-around-time, workload in the field, and complexity. Its client base was made up of unbanked and thin-file customers, so, automation based on traditional scores was not an option. Solution: Collect psychometric information for credit scoring which would allow a centralized, automated process.
• A non-traditional microlender wanted to obtain early warnings of clients that would likely fall in arrears on their next installment so that they could better focus pre-emptive collections efforts. By combining traditional repayment data with Android phone data, we are able to “rank” clients by the probability of next payment default. Now they can focus on the the one-third that will create 75 percent of the defaults.
• A traditional financial institution was turning down about one-third of applicants due to lack of credit history, and not belonging to the “right” demographics. They decided to invite “rejects” to re-apply by providing psychometric information, which allowed us to “rescue” about half of those prospects without increasing the default rate.
• A home appliances retailer providing $200 loans to consumers was losing clients due to the time required to verify their identity. By leveraging social network data, they have been able to reduce the approval turnaround time from two days to a few minutes in most cases. They have been able to approve more clients, reduce the cost of identity verification, and reduce cases of fraud.
2. Build on top of your current business
A good friend and a brilliant risk professional called me asking for help: “We are planning to launch a new product, for a new segment, in a new channel, so we need to use a new source of data to build an origination model.”
“Too many ‘news’ in the equation,” I told him. However, I joined his new venture.
You can guess how this adventure ended: slow volume uptake, lack of an actionable model after several months, and little enthusiasm to keep investing in order to capture value.
As we discussed in the first post, building models with alternative data is a numbers game. You need volume.
In the successful cases we mentioned before, we collected alternative data from a population that was already being served through a channel already established. This was to support a product with existing traction in the business. Innovation was concentrated in the data source and methodology to asses risk.
3. Leverage the best data source for you
Each source of data has advantages and drawbacks. In the front end, some sources may create more or less friction on the client onboarding, depending on origination processes. On the backend, usually the “low-friction” data is not structured. Unstructured data is not organized in a predefined way, so using it to build a risk model is more challenging than using structured data.
Once you have identified the pain point, you may work out with your partner/vendor the tradeoffs considering your population and channel. Note the following tips:
• Highly digital populations already served through an online channel may be approached using digital data, but you must make sure that you can get the volumes required to build a model based on unstructured data (unstructured data requires more volume to build a model).
• People with whom you already have an ongoing relationship may be a good population to leverage mobile phone data, as they may perceive a benefit to downloading and keeping your mobile application.
• Less digitized populations, served through traditional channels (branches or field loan officers) may be better suited for psychometrics.
Avoid the pitfall of falling in love with a specific data source and then figure out a use case within your business. Go the other way around: “given my business need, what data source better fits it?”
4. Partner with somebody that can handle multiple data sources
“When you only have a hammer, all problems look like nails,” my first boss told me a long time ago. To avoid the pitfall described on recommendation three, you must partner up with a vendor that can manage several data sources.
This will not only let you choose the right pain and business to focus on, but also give you flexibility as you roll out.
For example, we found, while working with a one client that their clients would willingly share their email data. Unfortunately, we found that they used their email so scarcely, that we couldn’t score many of them. Now we are working with psychometrics in this population.
In another situation, we started using psychometrics to approve more people at a Mexican e-lender. In the meantime—while they were approving more clients—we collected digital data from these same applicants. After several months, we have been able to combine both sources of data to approve even more people.
If you are like most of us and work for an organization that needs results in a few quarters, structure your initiative to collect early results that may give you inertia while you go for the long-term prize.
We work with an institution that provides big loans. They do not have that much volume, but they invest heavily in each prospect. Big stakes, low volume is the most challenging environment to build an alternative data-based score. It took us almost 4 years, but now they are harvesting the fruits of their perseverance.
To deal with this issue, you need to be creative to identify secondary pain points that may be addressed quickly along the way.
For example, we worked for a retailer that wanted to increase approvals while keeping defaults in line by approving new-to-credit consumers. Loans had mostly 24 to 36-month terms and most 60 days defaulters tended to recover. That was a challenging situation: we would have to wait 12 months for vintages to mature, and look for 90 or 120 days in arrears for the “bads” to profile. It looked like a 2 to 3 year project.
But we found a secondary pain: “straight rollers.” These were loan recipients who didn’t pay their first two or three installments and were eventually written off. We collected data on all their clients to quickly build a “straight rollers model.” We only needed 3 installments on each vintage to identify bads.
Along the way, we are collecting data that will be used to build an admission score to address the main pain.
In summary, building credit policies based on alternative data is challenging. Fortunately, there is enough learning accumulated in our community to avoid some pitfalls and we hope you find these tips useful.
By Brett Elliot, Director of Product, LenddoEFL
Lenders often use loan officers, promoters or other types of agents to sell loans, financing, and credit cards. This can be an effective way to acquire new customers but there is a big risk in this approach. Agent incentivisation plans will make or break your bottom line. Good incentive plans lead to large, healthy portfolios, but bad ones lead to fraud, default and chargebacks. We’ve worked with lenders all around the globe and have seen both good and bad strategies. Here are some of the best and worst.
Worst agent incentivisation strategies
Paying per applicant
When your goal is to get new customers, a very simple approach is to pay agents for each customer that applies. Obviously, the more people into the top of the funnel, the more customers you will get right? Not exactly. Agents will optimize for their commissions. First they will go after good borrowers, then they will go after anyone. And some of them will realize they can make up fake applicants so they don’t have to go after anyone at all. If you simply try to optimize the number of applications, you will get a lot of applications, few disbursements, and even fewer good customers. This may seem obvious but we know of a large commercial bank in South America that did this and they had a huge problem finding good borrowers.
Paying per approval
Since paying per applicant does not work, the next logical step is to pay per approval. This way your agents can still acquire as many people as possible but you keep the portfolio healthy with controls in your approval process. This is the most common approach to incentivizing agents but it does create a perverse incentive for the agent to game the system in order to get more approvals. For example, one lender in Mexico was offering credit cards to college students. At some point the agents discovered that the only criteria for approval was that the applicant be under 26 years old. This caused a huge spike in fraud as the agents signed up anyone they could find that met the age criteria. If you are going to pay per approval, the controls you set must be strong and difficult to game. See below for recommendations about how using technology can help.
Paying a commission higher than the loan interest
A subtle but often overlooked problem with any incentive plan is paying an agent a higher commission than the loan interest costs. We know of a digital bank in South America that does this and they are experiencing a type of perpetual fraud. In this scheme, the agent fills out an application using a stolen identity. Then he takes out another loan using a different stolen identity and pays back the first one with it. He repeats this process over and over, collecting his commission. This is only possible if the commission is higher than the interest rate so keep that in mind when making your commission schedule.
Good agent incentivisation strategies
Paying a percentage of the money collected
One approach that aligns the agents and lenders is to pay the agent a percentage of the money collected. This incentivizes the agent to sign credit worthy new customers but also to follow up with the customer and make sure they make their payments. We sometimes call this the “MFI model” because of how effective some MFIs have become by using it.
Withholding payment for a certain period of time
Paying a percentage of the money collected doesn’t work for credit cards, but a strategy that does is to withhold payment for a certain amount of time. For example, one financial institution we know of pays the agent a little when the credit card is activated and then the remainder in 4 months so long as the card is not in arrears.
Combine several incentives
There is no one size fits all for any company and often combining different incentives works really well. The companies with the healthiest portfolios that we have seen do this. For example, a large MFI in Mexico pays their agents a very low fixed salary, plus a percentage of the amount disbursed and a percentage of the money collected.
Use technology to strengthen the controls
If you are certain that your controls and risk models are effective at weeding out the bad borrowers, then paying per approval is a reasonable approach. But how certain are you especially if your customers do not have much credit history? Technology can definitely help in this area. Using highly predictive alternative credit scores is a great start. Be sure to ask the vendor what controls are in place to detect agent fraud since they will try to game the system. At LenddoEFL, our scores are backed by our Score Confidence system which looks out for suspicious loan officer behavior and coaching as well as other signs of fraud.
The end goal is to reward agents for portfolio growth and portfolio quality. Lenders that limit the number of new clients added per month, maintain a solid portfolio of recurring clients, and combine different incentives for compensating agents, seem to have the largest and healthiest portfolios.
Written by Rodrigo Sanabria, Director Partner Success, Latin America
On a prior post by Carlos del Carpio (“The Economics of Credit Scoring”), we discussed the business considerations to assess the merit of a risk model. In this post, I will address how a good origination model impacts the bottom line of a company’s P&L.
These principles may be adapted to look into other types of models used at later stages of a loan life, but on this post we will only address loan origination.
From a business point of view, an origination model is a tool that helps us aim at the “sweet spot”: where we maximize profits. A simple way to think about it is as a trade-off between the cost of acquisition (per loan disbursed) and cost of defaults (provisions, write-offs): The higher the approval rate, the lower the cost of acquisition, but the number of defaults go up.
How do we go about finding the sweet spot? I’ll try to explain it below.
A good model has a good Gini. A “USEFUL” model creates a steep probability of default (also known as PD) curve – we usually refer to it as a “risk split”.
Figure 1 shows the performance of a model based on psychometric information used by an MFI. The Gini (not shown in the graphic) is pretty good (0.28). The risk split is great: the people in the lower 20% of the score ranking are about 9 times more likely to default than those in the top 20%.
Knowing the probability of default for a given group, we may set a credit policy. Basically, we need to answer: “what would the default look like given an acceptance rate?”
We have re-plotted the same data in Figure 2, but now we express the probability of default in accumulated terms. Basically, the graph shows that if we were to accept 80% of this population sample, we would have a 4.5% PD, but if we were to accept 40%, the PD would go down 2 points to 2.5%.
Now, from a business point of view, we still do not have enough information to decide. Do we?
Where would the profit be maximized?
The total cost of customer acquisition is mainly fixed. Whatever we spend on marketing and sales to attract this population, will not change if we reject more or fewer applicants. So, the cost per loan disbursed would grow as we reduce the acceptance rate.
Of course, the higher the acceptance rate, the larger the portfolio, and the more interest revenue we get. BUT, the higher the provisions and write-offs. The combination of these 2 variables (cost of acquisition and net interest income) produces an inverted U-shaped curve that uncovers the “sweet spot”
The current credit policy is yielding a profit at 100% acceptance rate (see Figure 3) because the sample being analyzed corresponds to all the customers that were accepted (i.e. we have repayment data about them). So, the portfolio is profitable.
But the sweet spot seems to be shy of 60% acceptance rate. If this FI were to cut down its approval rate to that level, profits would increase by about a third, and its return on portfolio value would almost double. Of course, there are other considerations around market share and capital adequacy that may play a role in such a strategic decision, but the opportunity is clearly uncovered by the model.
In my experience, the sweet spot usually lies within 30%-70% acceptance rates, driven by marketing expenditures, interest rates, cost of capital, sales channels, and regulation.
What if the shape of the curve shows a continuous positive growth? The sweet spot is at a 100% acceptance rate! – have we reached risk karma? – Most likely, the answer is no (but almost!).
Most likely, we are leaving money on the table. Some business rule may be filtering people before they are scored. I have experienced this situation while working with lenders. For example, a traditional bank was filtering out all SMEs that had been operating for less than X years. This bias in the population was creating a great portfolio from a PD point of view, but there was clearly an opportunity to include younger businesses. As you can see in Figure 4, the maximum return on the portfolio was achieved at 60% approval rate, but they could increase profits by approving beyond the current acceptance rate. Depending on their cost of capital, it may be a good idea to expand the portfolio by approving more people.
In summary, think of your origination model as a business tool. Don’t stop at looking at Gini to assess a model’s merit. Understand how your profitability would be impacted by changes in your acceptance rate. If the PD curve is steep enough, you may capture quite a lot of value by applying the model to either reduce or increase your acceptance rate.
Capturing digital footprints using psychometrics can help FSPs reach the unbanked.
By Rodrigo Sanabria, Partner Success Director, Latin America, LenddoEFL
In a recent post on her report, Accelerating Financial Inclusion with New Data, Tess Johnson highlighted the huge opportunity that alternative data represents for the future of financial services. The simple fact that mobile and internet penetration have surpassed financial services penetration in most emerging markets hints at a big opportunity: many people who have had no meaningful access to formal financial services are creating digital footprints financial service providers can capture and analyze to reach them with commercially viable services that help them improve their lives. This prospect is also made possible thanks to machine learning and big data methods that were not available to us a few years ago.
For those of us in the world of financial inclusion, these are very exciting times: the simultaneous emergence of online penetration and data analysis methods is generating an opportunity that our predecessors in this field couldn’t even have imagined.
The bad news is that harnessing digital footprint data using machine learning is not easy; it requires time, commitment and skills that are in short supply. However, the good news is that those with the vision and endurance to leverage this opportunity will build a competitive advantage that will be sustainable for years to come.
When developing an alternative credit score based on traditional information (e.g., demographics, repayment data), analysts usually have historical data to design and train models. Through back testing, the credit scoring model is applied to historical data to see how accurately it would have predicted the actual results (i.e., loan repayment). We can get a pretty good sense of how the model will perform in the future and set up a credit policy accordingly. Yet, when we cannot use such traditional data sources, we are entering into uncharted territory.
Lacking prior information about our current customers’ psychometric profile or digital footprint, we must build those data sets from scratch. Depending on the data source, we may need very large data sets to compensate for the lack of data structure (unstructured data is simply data that is not easily accessible in a format or structure, like an Excel spreadsheet, that is optimal for generating insights). Just as with all other artificial intelligence applications, the more data you collect, the more predictive and stable your algorithms become. LenddoEFL is an example of an organization that gathers data for these profiles and footprints. It is an alternative credit scoring and verification provider that uses psychometric and other data about a loan applicant to determine a credit score and verify identity.
Furthermore, even state-of-the-art alternative data sources do not necessarily allow you to build models that are stable and reliable across multiple segments of the market. Therefore, you need to build algorithms that are specific to your target population.
One of the most challenging issues when implementing alternative data scoring initiatives is showing the results that can be achieved within a given set of time and budgetary constraints. In the long run, after the portfolio has matured, you can show whether using alternative data allowed you to approve more applicants within your target default levels, controlling by business cycle. But if you are working with 24- to 36-month loans, it may take three or four years before you can fully assess the impact of using alternative data, by which time internal attention spans may have already run short.
To account for that, LenddoEFL uses early indicators of model performance. We set a target maturity and days in arrears according to a financial institution’s portfolio’s profile, for example, 60 days in arrears within the first 9 months. Then we calculate a Gini coefficient—a scale of predictive power that can help lenders understand how good its credit score is at assessing who will repay and who will default on a loan (not to be confused with the Gini coefficient that measures income inequality) for the model as applied to that portfolio. (For more details on how to use the Gini, check out our blog series from our risk and analytics team: Part 1, Part 2, Part 3).
Is it too late to pursue an alternative credit scoring initiative? I would say yes, there are plenty of companies already doing this—Te Creemos in Mexico, Mynt in the Philippines and Business Partners in South Africa—but only a few lenders are utilizing alternative data in each market. You could be the first institution in your segment and country to implement such an initiative, and you can still take advantage of others’ experiences and learning.
The sooner you start collecting data and building models, the sooner you will be able to underwrite the unbanked segment better than your competition, and the longer the window of advantage will be. For those who start late, catching up with the early adopters will be a great challenge.
Originally posted on Lodex website.
Technology and data advancement is rapidly providing us with tools for greater and data-driven insights. We are looking at new ways to solve old, longstanding problems.
LenddoEFL is a fantastic example. Through data and tech, LenddoEFL provide financial service providers, all around the world, an alternative tool to help measure a consumer's creditworthiness.
We know that blockchain technology walks hand in hand with disruption and innovation, therefore, we wanted to hear what the pioneers at LenddoEFL's thought on this hot-topic.
We had a chat with Jeff Stewart, LenddoEFL's Co-Founder & Chairman, who shared some of his insights into the use of blockchain for companies and how it can impact consumers. Check it out!
"The new innovations are opening up the possibility of consumers having more control over who sees what information when and being able to track who has seen it."
1. How do you see Blockchain Technology supporting LenddoEFL’s business?
At LenddoEFL, we are convinced that blockchain is one of the most innovative technologies since the public internet. We are also convinced it opens up opportunities for further providing access to financial services, cheaper and more conveniently. Since we started LenddoEFL in 2011, we have been continually innovating, anticipating the future and exploring new and upcoming technology solutions, and blockchain is one of these.
We have already successfully deployed our solution in the Ethereum blockchain ecosystem, where we are able to seamlessly provide our services and automate decisioning in smart contracts. As distributed ledger technology is further developed to reduce friction across the customer lifecycle, we believe we can further help lenders make better decisions and extend financial services to the unbanked. Blockchains, smart contracts and new cryptographic distributed architectures will allow us to do this faster and with less friction.
2. Will Blockchain be helpful or a hindrance for consumers owning their own data? How do you see the help or hindrance affecting the consumer?
It is too early to say for sure, but the technology is evolving very quickly. The new innovations are opening up the possibility of consumers having more control over who sees what information when and being able to track who has seen it.
One critical part to remember is that although the Zero Knowledge Proof offers exciting opportunities, consumers face similar challenges that exist today with regard to understanding what data is being put on the blockchain. If a third party uses the blockchain thoughtfully, they will not include any personally identifiable information (PII), but rather just identifiers. This means that the consumer still has the right to be forgotten, and maintains the ability to control and delete their data.
On the other hand, if a third party puts your data or your PII directly on the blockchain, it is permanent and unalterable and potentially accessible to anyone. This is absolutely unacceptable in our view, and problematic for consumers.
With the rise of GDPR protecting European consumers’ data, the Facebook scandal, and at the same time PSD2 putting the consumer in charge of their financial data and allowing it to be shared, it will be interesting to see how the blockchain can facilitate better control and ability to share when so desired.
Jeff Stewart, LenddoEFL's Co-Founder & Chairman
3. Are there any projects that you are working on in the blockchain space that you can share?
We have been researching the blockchain for over 3 years and our team is actively working on a number of exciting projects. It’s too early to share the details but we are keenly interested to be part of the development in the blockchain space and will have more to share in the coming months.
4. How do you see BLOCKLOAN supporting your business in the future?
BLOCKLOAN is a new Banking-as-a-Platform using blockchain technology with a lot of potential for empowering customers with increased financial flexibility. We are excited to help grow the platform through new functions and features linked to identity verification and credit scoring.
Gini Coefficients and the Economics of Credit Scoring
On a global scale, billions of dollars in debt are granted every year using decisions derived from credit scoring systems. Financial institutions critically depend on these quantitative decision to enable accurate risk assessments for their lending business. In this sense, as with any tool that serves a business purpose, the application of credit scoring is not ultimately measured by its statistical properties, but by its impact in business results: how much can Credit Scoring help to increase the benefit and/or to decrease the cost of the lending business.
Assessing Credit Scoring from a business perspective could sound pretty obvious. However, given the typical compartmentalization of roles that could exist at lending institutions, where Risk and Modeling teams can be completely separated from Commercial departments, it could be easy sometimes to focus too much on the statistical aspects of credit scoring such as Ginis, and forget the ultimate business nature of its purpose. Although there is a clear positive relationship between economic benefits and predictive power, there are also certain elements that can affect the balance between costs and benefits. In this post, we discuss some of these elements and explain their role in the cost-benefit analysis of credit scoring.
The benefits of credit scoring
The benefit of credit scoring derives from its ability to accurately identify good customers, and discriminate them from bad customers. The more good customers a model can identify, the greater the interest income that can be generated from a credit portfolio. And the more bad customers it can discriminate, the lower the losses for the credit portfolio. In this sense, the economic benefit of credit scoring can be amplified by two things: the volume of customers, and the size of the credit disbursed to these customers.
Take for example the portfolio of microfinance institution “A” with several thousands of customers but very small loan amounts, and compare it against a smaller microfinance institution “B” providing loans of the same size to a portfolio of just a few hundred customers. Both institutions can see a similar increase of 1% in the predictive power of their credit scoring models, however, the increase in economic benefit yielded from this increase in predictive power will be different just because of the different sizes of portfolio volumes. Everything else being equal, the higher the volume of the portfolio, the higher the potential economic benefit of credit scoring.
The same can be argued for the size of credit disbursed to the customers of a portfolio. For example, take an SME lending institution with just a few thousands of customers but with relatively high credit amounts in the hundreds of thousands of dollars. An increase of 1% in predictive power could bring just a handful of new good clients into the portfolio, or avoid the disbursement of a handful of very bad loans. However a change in just a handful of good or bad clients can be enough to generate a considerable increase of economic benefit in the portfolio given the large size of the loans.
The costs of credit scoring
The costs of Credit Scoring can be split in two parts. First, the cost of developing a new model, and secondly, the cost of implementing and maintaining credit scoring models.
If we assume lending institutions are at a stage of technological maturity in which all the necessary data to create a credit scoring model exists and is continuously updated with certain level of quality and integrity, then the first type of cost just depends on the complexity of the modeling process. The whole process of building a model includes data extraction and cleaning, feature engineering, feature selection and the selection of a classification algorithm.
Depending on the lending institution, this process can be handled by a single data scientist (e.g. think of the CRO of a small Fintech startup), or it can be handled by a large department including many different teams with different roles such as data engineers, data scientists and software engineers (e.g. think of a large multinational bank). At the same time, the teams in charge of the model building process can be comprised of junior analysts fresh out of college using well-known standard techniques or include teams of PhDs in computer science doing advanced machine learning. At the end, the cost involved in developing the credit scoring models will depend on how much complexity and sophistication can be afforded and/or needs to be put into the process.
Once the model has been built, it also needs to be implemented and monitored over time. The costs involved are not trivial. Again, they will depend on the stage of technological maturity of the financial institution and the complexity and sophistication required. For example, in some cases the implementation of a credit scoring model can be as simple as creating an Excel calculator loaded with the coefficients of a logistic regressions where some values are manually inputted by a Loan Officer to get a score (e.g. think of a small MFI in the rural area of a developing country). Or it can be as complex as a Python package in a cloud-hosted decision engine integrated in the online platform of a large bank. The handling of big data, software development and testing, as well as the security and legal aspects involved in the deployment of a credit scoring system can considerably increase its costs. And all this, without even considering if the teams that will monitor the performance of the models implemented on a defined frequency basis are dedicated full time, or they are just the same team that also did the modeling and/or deployment.
Bottom-line: The statistical classification accuracy measured by Gini coefficients are indicative of some part of the benefits of using credit scores, but they are not the most important nor the final metric when assessing the cost-benefit of credit scoring. The reason is because the benefits of credit scoring can be influenced by the volumes of customers and the size of the credit. And the costs of credit scoring ultimately depends on the stage of technological maturity of the lending institution, as well as how much complexity and sophistication can be afforded and need to be put in the development, deployment and monitoring of credit scoring models.
So next time you need to make a decision about using Credit Scores to boost your lending business, ask how much they can help to increase the benefits of the business, and how much they can help to decrease its cost. The final decision will depend on a lot more than just Ginis.
At LenddoEFL, we have the expertise to help you boost the benefits and reduce the costs of credit scoring using traditional and alternative data. Contact us for more information here: https://include1billion.com/contact/.
By Satoko Omata | 10 July, 2018
TODAY, customers expect more from their banks – who are slow to deliver new products, services, and experiences as a result of their legacy systems and archaic processes.
However, those that truly want to meet and exceed expectations (and snatch up a bigger share of the market), there are a few lessons they can learn from fintechs.
By partnering with fintechs, banks would have access to new services that help deliver better offerings to customers, at cheaper rates.
At the Wild Digital conference on Wednesday, panelists at a discussion observed that of all the industries, those dealing with money-based investments have been the least changed by technology.
The panel featured Richard Eldridge, Co-founder and CEO of Lenddo EFL; Ashley Koh, Senior Vice President and General Manager of Send, Matchmove; Michele Ferrario, Co-Founder and CEO of StashAway; and Gan Pooi Chan (PC), Country Director GoBear.
Lima, Peru, 16 de julio del 2018 – Organizaciones se han unido para financiar una alternativa innovadora de evaluación crediticia para incrementar el acceso a financiamiento a jóvenes que no cuentan con posibilidades de acceder a financiamiento que recién están comenzando un negocio.
Cuatro instituciones se han unido para invertir y avanzar en la innovación liderada por jóvenes en el Perú. Los jóvenes emprendedores que tienen dificultades para acceder a crédito debido a la falta de historial crediticio, ahora pueden solicitar un préstamo de negocios de la institución financiera peruana Caja Sullana usando la evaluación de crédito psicométrica de LenddoEFL, una fintech que potencia las decisiones basadas en datos alternativos para promover la inclusión financiera.
Fundación CITI financia esta iniciativa como parte de sus esfuerzos para impulsar las iniciativas empresariales de los jóvenes en los mercados emergentes. COPEME, una organización peruana que promueve la inclusión financiera, gestiona este proyecto como potencial para expandir esta tecnología a otras instituciones financieras del país.
Caja Sullana, que actualmente usa la evaluación psicométrica de LenddoEFL tanto en agencias como online, buscaba una forma de aprobar más personas con poca información crediticia de manera más sencilla. El proceso previo de evaluación crediticia implica visitas y análisis por los oficiales de crédito que consumen tiempo, que muchas veces tienen como resultado el rechazo del préstamo. Con la evaluación de LenddoEFL, Caja Sullana puede agilizar su proceso de solicitud de crédito, reducir la carga de trabajo para los oficiales de crédito, y tomar decisiones más informadas sobre los solicitantes con poca información.
“Citi Perú está comprometido con el empoderamiento económico de las comunidades donde vivimos y trabajamos, por eso promovemos este programa que fortalece a las microempresas y promueve las micro finanzas. Este proyecto constituye una excelente iniciativa para estimular el uso de tecnologías y para promover la inclusión financiera en las comunidades más alejadas”, señaló Camila Sardi, Head de Asuntos Públicos de Citibank del Perú.
"Dos de nuestros ejes estratégicos son el apoyo a la inclusión financiera de más peruanas y peruanos, en particular de zonas rurales y peri-urbanas, y la implementación de soluciones innovadoras que mejoren la eficiencia de las instituciones de micro finanzas: En ese sentido, el proyecto ejecutado con el apoyo de Fundación Citi, se suma a las acciones que en el marco de estos dos ejes desarrollamos en el país, habiendo encontrado en Caja Sullana y LenddoEFL, dos organizaciones cuyo alcance, experiencia y objetivos facilitan la consecución del propósito de su diseño y puesta en marcha: la incorporación al sistema financiero de jóvenes emprendedores a través del empleo de una herramienta disruptiva que estamos seguros tendrá un impacto significativo." afirma Carlos Ríos Henckell, Gerente General de COPEME.
“Tenemos como objetivo atender a los segmentos más jóvenes y ofrecerles esta nueva opción para ingresar al sistema financiero, considerando su perfil como emprendedores en potencia. La falta de historial crediticio dificulta el acceso a herramientas de desarrollo, por lo que nos esforzamos en promover la inclusión financiera y ser el soporte económico que ellos necesitan”, expresó el presidente del Directorio de Caja Sullana, Joel Siancas Ramírez.
Además, agregó “nuestro sentir como institución siempre ha sido acompañar a los ‘peruanos guerreros’ en el crecimiento de sus proyectos y ser parte importante en la historia de su éxito”.
“Trabajamos con algunas de las mayores instituciones financieras de Perú y América Latina, esta es una oportunidad única de servir a la inclusión de jóvenes emprendedores. La evaluación de LenddoEFL ofrece una forma poderosa de incluir a más personas en el sistema financiero, y estamos entusiasmados de asociarnos con COPEME, Fundación Citi y Caja Sullana para servir mejor a los jóvenes emprendedores de todo el país”, señaló Rodrigo Sanabria, Director Partner Success, América Latina, LenddoEFL.
Acerca de Citi
Citi, el banco líder global, tiene aproximadamente 200 millones de cuentas de clientes y realiza negocios en más de 160 países y jurisdicciones. En el Perú, Citi ofrece a corporaciones, gobiernos e instituciones una amplia gama de productos y servicios financieros, incluyendo servicios bancarios y de crédito, servicios bancarios corporativos y de inversión, corretaje de valores, servicios de transacción y administración patrimonial. Por información adicional, visite: www.citigroup.com
Acerca de COPEME
Somos una organización que desarrolla actividades y provee servicios para el fortalecimiento del sector microfinanzas, el desarrollo de la Mype, y el fomento de la inclusión financiera. Trabaja en Perú desde 1991, alcanzando sus acciones a microfinancieras de todo el país, empresas privadas, organismos públicos, proveedores de fondos, inversionistas y otros actores relacionados al segmento Mype y de microfinanzas. http://www.copeme.org.pe/
Acerca de Caja Sullana
Somos la Caja Municipal de los emprendedores con norte, tenemos ya más de 30 años en el Sistema Financiero regulados por la Superintendencia Nacional de Banca y Seguros. Actuamos bajo la forma de Sociedad Anónima, con el objetivo de captar recursos y utilizarlos para brindar diferentes servicios financieros, preferentemente a las pequeñas y micro empresas, contribuyendo así al desarrollo económico en las diferentes regiones donde operamos, siempre comprometidos en ofrecer estos servicios con alto sentido de Responsabilidad y Calidad. Más información sobre nosotros o nuestros servicios: http://www.cajasullana.pe.
Acerca de LenddoEFL
Nuestra misión es proveer a mil millones de personas acceso a poderosos productos financieros a un menor costo, más rápido y conveniente. Usamos Inteligencia Artificial y Análisis Avanzado para traer las mejores fuentes de digital y psicometría para ayudar a las instituciones financieras en países en desarrollo para atender en confianza a las personas que no están bancarizadas y pequeños negocios. A la fecha, LenddoEFL ha proporcionado productos como puntajes crediticios, verificación e Insights a más de 50 instituciones financieras, ayudando a siete millones de personas e impulsando el préstamo de dos mil millones de USD. Para mayor información, visite https://include1billion.com/.
SINGAPORE (PRWEB) JULY 11, 2018
LenddoEFL, a fintech offering alternative credit scoring and verification solutions in emerging markets, welcomed Jefri Sormin as its new Indonesia Country Director.
“I’m joining LenddoEFL to help give more people access to credit and banking services, and to drive growth in Indonesia,” said Jefri Sormin, Indonesia Country Director, LenddoEFL. “Indonesia is home to 260 million people and the Financial Services Authority (OJK) has aggressive financial inclusion targets. Our solutions can help Indonesia achieve those goals, while helping banks serve more underbanked people with less risk.”
Jefri has over 15 years of experience in banking, including Citibank, General Electric, Sewatama and Orica. As Country Director for Indonesia at LenddoEFL, Jefri will be responsible for bringing the company’s credit scoring, verification and insights products to financial institutions in the country actively helping them to successfully achieve digital transformation.
“Indonesia is poised for continued growth in financial access and services,” said Mark Mackenzie, APAC Managing Director, LenddoEFL. “We are already seeing strong demand from Indonesian financial institutions for innovative ways to responsibly serve more people. I’m confident that Jefri’s leadership and brand-building skills will help us meet the demand in Indonesia.”
LenddoEFL’s mission is to provide 1 billion people access to powerful financial products at a lower cost, faster and more conveniently. We use AI and advanced analytics to bring together the best sources of digital and behavioral data to help lenders in emerging markets confidently serve underbanked people and small businesses. To date, LenddoEFL has provided credit scoring, verification and insights products to 50+ financial institutions, serving over 7 million people. Find more information at https://include1billion.com/.
The relationship between Gini Coefficients and Acceptance Rate
One of the most frequent uses of Credit Scores is to decide whether to admit or reject an applicant applying for loan. This is usually called an “Admission score” or “Origination score”. A key decision around this use case is the selection of a score cut-off that will determine a threshold for admission. This cut-off value determines the acceptance rate of the population.
If the score is working well and predictive power is good, the relationship between acceptance rate and default rate will be positive. The higher the acceptance rate, the higher the default rate of the accepted population and vice versa. The direction of this relationship also has two implications: when acceptance rate is higher, the absolute number of bad loans (i.e. non-performing loans) or “bads” will also be higher, and the proportion of these “bads” in respect to the total loans in the accepted population will be higher too.
What does this mean in practical terms?
It means that the predictive power as measured by a Gini coefficient for the exact same score at different levels of acceptance rate for the exact same population will be different. The higher the acceptance rate, the higher the Gini coefficient and vice versa.
This is something that can be easily tested. If you have a portfolio and a score with good predictive power, you can calculate the Gini coefficient for different score cutoffs or acceptance thresholds and the results should look something similar to this example of a typical credit portfolio:
So for example, if there is a change in credit policy and the acceptance rate is lowered from 60% to 40%, the Gini coefficient for the same score over the new sample may also be lower. Does that mean the model is not working anymore? Absolutely not. All the contrary, it’s probably just a good signal that the score is doing a good job. Once a change in acceptance rate is implemented, results should be assessed by the change in default rate, not in predictive power.
Bottom-line: To judge the predictive power of a Credit Score by the means of Gini, you also need to take into account the Acceptance Rate at which the Gini coefficient is measured. Lower Acceptance Rates will tend to have lower Gini coefficients by construction, even if it is the same exact score over the same population.
The fundamental reason behind this phenomenon was discussed in the part 2, where we explained why Gini coefficients should only be directly compared over the exact same data samples, even if the two samples correspond to the same population.
By Jonathan Winkle, Behavioral Sciences R&D Manager, LenddoEFL
An experimental psychologist by training, I am relatively new to the world of financial technology. Since joining LenddoEFL, I have embraced terms like information asymmetry, alternative data credit scoring, and financial inclusion. Yet it was only during a recent trip to the field that I was able to meet the people behind the FinTech jargon we use in our day-to-day, the small business owners whose lives we help improve in our mission to #include1billion.
In April of this year, I traveled with colleagues to Veracruz, Mexico to test new psychometric content for one of the top 3 microfinance institutions (MFI) in the country. Their group loan product extends a line of credit to a collection of business owners, but liability for payments is joint: if one person misses a payment, the group must still make that payment in full. Since many of those applying for these loans lack traditional credit histories, this MFI asked LenddoEFL to develop psychometric exercises that could quickly and reliably assess group traits that predict creditworthiness.
There are traits that define a strong social group which are nonexistent for individual borrowers. A successful group has strong internal relationships that ensure they will help each other in times of need. A tenacious group can generate creative ideas to solve problems that arise when life presents hardships, as it is wont to do. And a cohesive group exhibits decision making abilities that allow it to act deliberately and with confidence. We designed new psychometric exercises to measure these core traits, and tested them in the field with groups of small business owners applying for loans.
Measuring interpersonal relationships through social pressure
To measure the strength of a group’s interpersonal relationships, we examined the social pressure that exists among group members. Do individuals feel that they can answer sensitive questions honestly? Or do they feel pressure to conform to the opinions of the group majority? While the group was sitting together in one room, we asked them to raise their hands if they agreed with statements about the trustworthiness, fairness, and helpfulness of their local communities. We then asked individuals to answer these questions privately. The discrepancy between how the questions were answered in each setting could reveal how much social pressure exists, and thus how comfortable group members are being honest with each other. We expect that less social conformity means the group’s interpersonal relationships are stronger, an important factor for predicting whether the group will cover individuals who may miss payments throughout the loan cycle.
Measuring creativity through brainstorming
To measure a group’s creativity, we created a set of generative exercises. For both an easy and a hard problem, we had groups brainstorm as many solutions as they could in 60 seconds. The number of solutions generated was recorded as a creativity metric, and, as predicted, groups generated many fewer ideas for the harder exercise. We were also interested in the group’s dynamic as they performed these tasks. Were they apathetic or engaged? Was there a dominant member of the group? Ultimately, when a loan payment is due and some individuals are short on money, can the group come up with ideas for how to get the extra money? We hope that these generative exercises will shed light on this critical group trait.
Measuring decision making abilities through consensus
To measure a group’s decision making abilities, we created a time-to-consensus task. This exercise asks the group to solve a problem where all members must agree on the answer they provide. While we asked the groups to estimate the population of the state they live in, we actually don’t care how accurate their answer is! What’s more important in this exercise is how the group reaches consensus. Are they indifferent and accept the first estimate suggested? Or do they take their time and argue intensely while deliberating over possible solutions? What kind of strategies did they use to reach their estimate? Importantly, this task provides loan officers with a window into the group dynamic that might not otherwise be seen if the assessment merely collected static information such as sociodemographics and business revenues.
Financial inclusion is the mission of LenddoEFL, but working directly with the people we want to include allowed me to better understand how our assessments must be tailored to their cultures and experiences. The better we can measure group dynamics that predict creditworthiness, the more successfully we can extend financial services to those in need. As we continue to expand our credit scoring offerings across the world, looking past the business jargon we use and maintaining empathy for the humans we touch is essential on our path to #include1billion.
By: Carlos Del Carpio, Director of Risk and Analytics, LenddoEFL
This is part 2 of a series of blog posts about Ginis in Credit Scoring. To see the part 1, follow this link.
What is an AUC?
AUC stands for “Area Under the (ROC) Curve”. From a statistical perspective, it measures the probability that a good client chosen randomly has a score higher than a bad client chosen randomly. In that sense, AUC is a statistical measure widely used in many industries and fields across academia to compare the predictive power of two or more different statistical classification models over the exact same data sample .
How is AUC used in Credit Scoring?
In the particular case of Credit Scoring, AUCs are useful for example in the model development process, when there are several candidate models built over the same training data and they need to be compared. Another typical use is at the time of introducing a new credit score, to compare a challenger against an incumbent score over the same sample of data under a champion challenger framework.
How does AUC relate to Gini Coefficient?
The Gini Coefficient is a direct conversion from AUC through a simple formula: Gini = (AUC x 2) -1. They measure exactly the same. And it is possible to go directly from one measure to the other, back and forth. The only reason to use Gini over AUC is the improvement in the scale’s interpretability: while the scale of a good predicting model AUC goes from 0.5 to 1, the scale in the case of Gini goes from 0 to 1. However, all the properties and restrictions of AUC still translate into Gini Coefficient, and this includes the need to compare two different AUC values over the exact same data sample to make any conclusion about their relative predictive power.
What does this mean in practical terms?
Any direct comparison of the Gini Coefficients (or AUCs) of two different models over two different data samples will be misleading. For example: If a Bank A has a Credit Score with a Gini Coefficient of 30%, and Bank B has a Credit Score with a Gini Coefficient of 28%, it is not possible to make any conclusion about which is better or which is more predictive because they have been calculated over different data samples without accounting for the difference in absolute number of observations and the difference in proportion of good cases against bad cases. The only direct comparison possible is the one made about two scores side by side, over the exact same data sample.
Bottom-line: To affirm that a certain absolute level of AUC or Gini Coefficient is “good” or “bad” is meaningless. Such affirmation is only possible in relative terms, when comparing two or more different scores over the exact same data sample. Unfortunately this is often not well understood, which leads to the most frequent misuse of AUC and Gini Coefficients, such as direct, un-weighted comparisons of Gini values across different samples, different time periods, different products, different segments and even different financial institutions.
 Hanley JA, McNeil BJ. The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology, 1982, 143, 29-36.
Over years of blogging, one of our most popular ever blog posts was about the Gini coefficient. In this series of posts, we revisit the Gini and dig further into its uses and the ways we see it misused in credit scoring.
What is a GINI?
For lenders around the world, the “Gini Coefficient” is an often heard, sometimes feared, and frequently misunderstood statistical measure. Commonly used to assess things like wealth inequality, Gini Coefficients are also used to evaluate the predictive power of credit scoring models. In other words, a Gini Coefficient can help measure how good a credit score is at predicting who will repay and who will default on a loan: the better a credit score, the better it should be at giving lower scores to riskier applicants, and higher scores to safer applicants.
Though calculating a Gini Coefficient is complex, understanding it is fairly simple:
A Gini Coefficient is merely a scale of predictive power from 0 to 1, and a higher Gini means more predictive power.
However, there are a few key aspects of Gini Coefficients that are not always well understood and can lead to their misuse and wrong interpretation. Over this series of blog posts we’ll discuss four of them:
People often compare Ginis when they should not. The only useful comparison across Ginis (or AUCs) is when looking at different scores over the exact same data.
People forget that Gini will vary by acceptance rate. When presented with a Gini coefficient, always keep an eye on the effect of the acceptance rate.
People focus on Ginis, but are not always aware of its impact on the costs, benefits and overall economics of Credit Scoring.
People do not fully understand and often overestimate the role of Gini in the business of lending.
About the Author:
Carlos del Carpio is Director of Risk & Analytics at LenddoEFL. He has 10+ years of experience developing credit scoring models and implementing end-to-end credit risk solutions for Banks, Retailers, and Microfinance Institutions across 27+ countries in Latin America, Asia and Africa.
LenddoEFL’s mission is to provide one billion people access to powerful financial products at a lower cost, faster and more conveniently. We use AI and advanced analytics to bring together the best sources of digital and behavioural data to help lenders in emerging markets make data-driven decisions and confidently serve underbanked people and small businesses. To date, LenddoEFL has provided credit scoring, verification and insights products to over 50 financial institutions, serving seven million people and lending two billion USD. For inquiries about our products or services please contact us here.