Data-First Risk Modeling Is Empowering A New Lending Era


Michel Kilzi 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 not have access to basic financial services that can set them on a path to improve their future? In 2017, an estimated 1.7 billion adults did not not have bank accounts, despite having finances to manage, from cash to utility payments.

Traditional underwriting efforts revolve around outdated criteria or credit scoring metrics that don’t fit marginalized people, which means that potentially good prospects have been denied the life-changing benefits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 granular visibility into the credit profile of potential borrowers. Traditional wisdom says that an unknown factor is a source of risk, but today’s cohort of emerging consumers already has a digital footprint, ensuring an abundance of valuable alternative data that can reveal if they might be creditworthy candidates. Hence, building an interconnected data-first financial ecosystem, where unbanked people can confidently use financial services for the first time, will boost financial inclusivity.

The Equation For A Single Version Of The Truth

The way to serve credit-invisible or unbanked people is best described through a simple equation:

(Data Inflow + Relationship Discovery) * Predictive Scoring = Financial Inclusivity

Borrowing language from computerized business management, I like to call this the equation for a single version of the truth. Using this equation, financial service providers can begin to iterate on forms of alternative profiling for previously unbanked customers. The data is out there and ready to be connected to the larger financial ecosystem as more and more unbanked have mobile phones and are digitally transacting in a way or another. We just need to transform this raw and noisy data into valuable intelligence that decreases barriers to entry and replaces highly exclusive qualification criteria to ensure inclusivity of new-to-credit customers.

Data Conduits Inflow

In this increasingly connected world of mobile phones and digital footprints, most consumers leave behind valuable data that reflect their behavior, lifestyle, income level and financial capabilities. These alternative data sets are amassed from various non-conventional origins, such as utility bill payments, metadata from mobile devices, social media, web and app usage and many other different types of interactions. Consumer data-sharing consent constitutes a first step to connecting a person’s different data conduits and creates an entry point for potential emerging consumers.

Once those different data touchpoints are connected, they converge with new sources of data to build an alternative customer profile. This takes us beyond the simple Know Your Customer (KYC) methodologies most people are familiar with. These data conduits shall constitute a first touchpoint to reveal real identities and profiles based on specific attributes stemming from obtainable data.

Relationship Discovery Modeling

While these separate data conduits offer a wealth of opportunities on their own, 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 that go beyond traditional credit scoring. These might include a qualification algorithm to unveil which lending product best matches a particular borrower, or a loan affordability algorithm, or a debt spiral protection algorithm to avoid over-indebtedness. The advancement of AI can enable new relationships between different data touchpoints while avoiding preconceptions by matching demographic findings, geo-localization and transactional data.

AI-Driven Predictive Modeling

Predictive models help in assessing a customer’s ability and willingness to pay back a loan. AI will sift through the overwhelming data to identify variables or clusters of variables that impact an individual’s alternative score. By providing a Single View Truth score, it will allow loan officers to review the full story of an applicant’s situation and make an informed decision. The art of insightful decision-making has always been part of the lending process, but now the science will grow to include more good data to support it.

The feedback loops linked to those models will improve accuracy over time, continually enrich every profile and refine the scoring models. By building machine-learning capabilities right in the models, we can leverage powerful technology to achieve true data harmonization. Harmonized data will unlock a full view of a person’s creditworthiness based on factors completely outside the realm of traditional financial service providers.

There will still be a challenge to reach those with no financial history or data conduits; hence it’s imperative that people help themselves before financial institutions can help them. Designing credit-builder tools for people with no scoring and light profiles is a practical fix that lets them start small and provides proof they can handle larger sums.

The Future Of Financial Inclusivity

As more financial aggregation tools enter the market, individuals will be able to have more control over their data conduits and manage their alternative profiles. I predict social lending picking up around Rotating Social Credit Associations (ROSCA) as another opportunity for people to leverage social credit. Obviously, alternative data conduits will push 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 will be the speed with which companies are able to build and adopt these new structures for an interconnected financial ecosystem and the level of convenience companies can offer customers. However, with so many ways to expand the availability of financial services, there will be plenty of new customers to keep the industry strong as a whole.


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