In an exclusive interaction with Adlin Pertishya Jebaraj, Correspondent of Finance Outlook, Predeep Chauhan and Mohit Jain, Founder & Co- Founder of Finfinity highlights how integrated analytics and digital public infrastructure help to increase financial stability and support long-term development. They stress the need of data-driven, real-time governance systems to control delinquency in fast-expanding digital lending models.
Pradeep Chauhan is an expert in transforming how consumers access finance through technology. He has been named an Innovator of the Year and a Strategic Leader in building Data-Driven lending solutions that meets the diverse needs of clients. Mohit Jain has an extensive experience in the Financial Services Industry. He has successfully scaled revenues through implementation of innovative digital financial solutions that improve engagement with customers and help financially support their business objectives.
How do you see the rise of unsecured lending impacting portfolio yield, risk-adjusted returns, and long-term capital allocation strategies?
Having worked through multiple credit cycles, I have seen how higher yields in unsecured lending can create false confidence when risk is not priced appropriately. These products lift yields but are fragile. Small changes in borrower behavior or liquidity can quickly raise delinquencies.
The challenge is not generating yield, but protecting it through cycles. This shapes capital allocation. Unsecured lending requires tighter origination standards, conservative limits, and forward-looking buffers. It works best as a calibrated portfolio component, supported by secured assets that bring stability and protect long-term, risk-adjusted returns.
Unsecured lending improves the headline yields but also increases volatility in earnings and capital use. In my experience, the real impact becomes clear under stress, when losses surface more quickly, and funding costs rise. This makes the gap between nominal and economic returns visible.
Capital allocation, therefore, needs to account for downside risk and regulatory expectations. I see unsecured lending working best when it scales within precise limits, supported by stress testing and ongoing monitoring. Over time, portfolios with a strong secured base and selective unsecured exposure deliver more consistent and durable returns.
How should lending leaders rethink financial governance models to manage rising delinquencies, especially in high-growth digital lending segments?
Rising delinquencies show that scale without control compounds risk. From my perspective, governance needs to move closer to origination and early warning signals. Static reviews must give way to continuous, data-led oversight. I see real-time monitoring, dynamic scorecards, and clear exposure limits as essential to containing stress early.
Another equally important aspect is cross-team integration. Risk, operations, and collections need to stay aligned to shorten feedback loops. Transparent communication and flexible repayment options often reduce losses more effectively than aggressive recovery. Strong governance comes from clear guardrails, consistent discipline, and the ability to adjust quickly as borrower behavior and conditions evolve.
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As delinquencies rise, governance must shift from control to decision support. In my view, digital lending moves too fast for delayed responses. Integrating data across finance, risk, and operations is critical for early stress detection. Delinquency trends should guide provisioning, capital buffers, and funding plans.
Governance also needs clear accountability across underwriting and collections. I believe that explainable decision models and ethical recovery are critical to protecting both financial outcomes and credibility. Together, these elements support predictable growth and capital resilience.
In a market where lending spreads are tightening, how do you evaluate debt versus equity funding to optimize long-term ROE?
When spreads tighten, the choice between debt and equity shifts from maximizing leverage to protecting returns. In my view, debt can still lift ROE when cash flows are predictable, and margins can absorb higher interest costs. This is where stress testing matters. I have seen that when small shocks begin to strain repayments, leverage quickly turns negative.
Equity, though dilutive, offers flexibility in uncertain conditions, while debt is most effective when visibility is high. As outcomes become less certain, I believe equity becomes the more sensible choice. Long-term ROE is protected by capital structures that hold up under stress, not by those that appear optimal only in stable conditions.
In a tightening spread environment, optimizing ROE requires looking beyond the cost of capital. I examine how leverage affects net income across different scenarios. Debt can enhance ROE through leverage, but higher interest costs reduce net income. In my opinion, when this pressure outweighs the benefits of leverage, ROE begins to decline.
Breaking ROE into margins, asset efficiency, and leverage helps evaluate how funding choices affect the balance sheet. I find debt works best when earnings visibility supports higher capital intensity. As volatility increases, equity provides balance sheet flexibility. The objective is to preserve return stability over time, not to optimize leverage at a single point in the cycle.
What structural financial efficiencies must leaders prioritize to bring down the high operating costs associated with customer acquisition, fraud mitigation, and collections?
High operating costs usually reflect fragmented processes. The focus should be on bringing data and technology together across the customer journey. Shared visibility into performance makes inefficiencies easier to identify and address.
For acquisitions, smoother onboarding and better targeting reduce rework. In fraud mitigation, real-time pattern detection reduces losses and the need for downstream cleanup. Across collections, segmentation and structured workflows reduce cost per account.
Routine tasks can be system-led, allowing teams to focus on judgment-heavy cases. I see structural efficiency coming from simpler processes, shared data, and more effective use of people’s time.
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Structural cost reduction starts with stronger data use and tighter operating discipline. From my perspective, integrating analytics across acquisition, fraud, and collections is key to visibility into spend and outcomes.
Standardized processes reduce duplication and help add some predictability. Unified fraud prevention and response frameworks help contain losses. In collections, I see better outcomes when effort is prioritized based on risk and recovery potential, rather than uniform follow-ups. A greater focus on retention also eases acquisition pressure. Together, these efficiencies create a leaner model that scales more reliably.
How will the next wave of digital public infrastructure reshape capital flows, lending spreads, and financial risk distribution across the ecosystem?
Digital public infrastructure will reshape how capital moves and how risk is priced. From my perspective, digital identity, interoperable payments, and consent-based data sharing help reduce friction and improve underwriting confidence. This opens access to segments that were earlier difficult to assess, such as small businesses, self-employed professionals, and first-time borrowers.
Lending spreads should also tighten as better data improves credit assessment and reduces reliance on physical collateral. I see risk dynamics shifting, with decisions increasingly driven by cash flows rather than assets. This widens access, but requires closer monitoring. At the same time, shared digital rails increase concentration risk, making strong governance and oversight critical for long-term stability.
Digital public infrastructure is reshaping how financial systems allocate capital and manage risk. I see standardized identity, payments, and data-sharing layers as critical to reducing structural inefficiencies and improving market confidence. This supports steadier capital flows and encourages broader participation across the ecosystem.
As information disparities narrow, lending spreads are likely to compress. I expect credit risk will be assessed more through verified data than asset backing. While this expands access, it also concentrates operational and cyber risk within shared platforms. Maintaining stability will depend on robust regulation, oversight, and system-level safeguards.
