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Data-first risk modeling enables a new credit era

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Michel Kilzic Serial entrepreneur, data intelligence expert with a vision to redefine the role of data as an asset in today’s new economy

Lending is the oldest financial product on Earth – so why do so many people in the modern world still lack access to basic financial services that can put them on the path to bettering their future? In 2017, it was estimated that 1.7 billion adults had no bank accounts, despite having to manage finances from cash to payments to utility companies.

Traditional underwriting efforts revolve around outdated criteria or credit score metrics that don’t fit marginalized people, meaning potentially good prospects have been denied the life-changing benefits of financial inclusion because they remain “invisible.” The problem for lenders is an information gap: a lack of detailed understanding of the credit profile of potential borrowers. Traditional wisdom says that an unknown factor is a source of risk, but the current cohort of emerging consumers already have a digital footprint, providing a wealth of valuable alternative data that can reveal whether they could be creditworthy candidates. Therefore, building an interconnected, data-first financial ecosystem, where unbanked people can confidently use financial services for the first time, will drive financial inclusiveness.

The equation for a single version of the truth

The way to serve people with no credit or no bank account can best be described with a simple equation:

(Data Inflow + Relationship Detection) * Predictive Scoring = Financial Inclusion

Borrowing language from automated business management, I like to call this the equation for a single version of the truth† Using this equation, financial services firms can begin to iterate forms of alternative profiling for unbanked customers. The data is available and ready to be connected to the larger financial ecosystem as more and more unbanked people have mobile phones and carry out digital transactions in one way or another. We just need to turn this raw and noisy data into valuable information that lowers barriers to entry and replaces the highly exclusive qualification criteria to ensure the inclusivity of new customers with credit.

Inflow of data channels

In this increasingly connected world of cell phones and digital footprints, most consumers leave behind valuable data that reflects their behavior, lifestyle, income level and financial capabilities. These alternative data sets come from a variety of non-conventional origins, such as utility bill payments, mobile device metadata, social media, web and app usage, and many other different types of interactions. Consent to consumer data sharing is a first step in connecting a person’s different data channels and creates an entry point for potential emerging consumers.

Once those different data touchpoints are connected, they come together with new data sources to build an alternative customer profile. This takes us beyond the simple Know Your Customer (KYC) methodologies that most people are familiar with. These data channels will provide a first point of contact to reveal real identities and profiles based on specific attributes arising from available data.

Modeling Relationship Discovery

While these individual data channels offer a wealth of possibilities in their own right, we need to know more about what each piece of data represents. When we connect the dots, we can create a 360-degree profile of each person.

Aggregated and enriched data can be injected into new disruptive scoring models beyond traditional credit scoring. These may include a qualifying algorithm to reveal which loan product is the best fit for a particular borrower, or a loan affordability algorithm, or a debt spiral protection algorithm to prevent over-indebtedness. The advancement of AI can enable new relationships between different data touchpoints, while avoiding bias by aligning demographic findings, geolocation and transactional data.

AI-driven predictive modeling

Predictive models help assess a customer’s ability and willingness to repay a loan. AI sifts through the overwhelming data to identify variables or clusters of variables that affect an individual’s alternate score. Providing a Single View Truth score allows loan officers to see the full story of an applicant’s situation and make an informed decision. The art of insightful decision making has always been a part of the lending process, but now the science will grow to incorporate more good data to back it up.

The feedback loops associated with those models will improve accuracy over time, continually enriching each profile, and refining the scoring models. By building machine learning capabilities directly into the models, we can use powerful technology to achieve true data unification. Harmonized data will provide a complete picture of a person’s creditworthiness based on factors that are completely outside the realm of traditional financial services firms.

It will still be a challenge to reach people without financial history or data channels; therefore, it is imperative that people help themselves before financial institutions can help them. Designing credit-building tools for those without scoring and light profiles is a practical solution that allows them to start small and prove they can handle bigger sums.

The future of financial inclusiveness

As more financial aggregation tools hit the market, individuals will have more control over their data channels and manage their alternative profiles. I predict that social loans will rise around Rotating Social Credit Societies (ROSCA) as another opportunity for people to leverage social credit. It is clear that alternative data channels will drive fintech players to innovate to unlock Good Data from the Big Data craze of the past.

There may be some resistance from telecom operators who are still struggling to shape a dominant position in this new ecosystem. But this won’t last forever given the growth of personal data sovereignty. The biggest arenas for competition are the speed with which companies can build and deploy these new structures for an interconnected financial ecosystem and the level of convenience that companies can provide to customers. However, with so many ways to expand the availability of financial services, there will be plenty of new customers to keep the industry as a whole strong.


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