A prominent example of this transformation comes from Scienaptic AI, a New York-based firm in AI-powered credit underwriting. In January 2025, Scienaptic announced its Credit Union Service Organization (CUSO) secured strategic equity investments from four new partners. The milestone showed Scienaptic’s growing influence in the financial technology sector.

This development aligns with the broader trends seen in the finance industry, where AI adoption is reshaping credit risk management. Just as AI is enhancing underwriting accuracy and streamlining processes for financial institutions globally, Scienaptic’s solutions focus on utilizing AI to unlock better decision-making and more inclusive lending.

Their ability to integrate advanced AI models for real-time risk assessment plays a crucial role in helping institutions expand credit access and maintain operational agility, even amid changing market conditions.

In this interview, we speak with Joydip Gupta, a business leader and management consultant with over two decades of experience across the USA, Singapore, and India. Currently serving as the APAC Head at Scienaptic Systems, Joydip has deep expertise in fintech, analytics, and AI-led transformation.

At Scienaptic, he focuses on deploying AI-based credit decisioning platforms across Asia-Pacific, helping financial institutions modernize and automate their underwriting processes.

In this conversation, AsiaTechDaily discussed the impact of AI on credit risk, the company’s expansion plans, and how Scienaptic is helping smaller institutions enhance their lending capabilities.

With rising interest rates and tightening liquidity, how are lenders in APAC adapting their credit strategies—and what role is AI playing in navigating this new normal?

As interest rates rise, the cost of funds increases for lenders. This often leads to a drop in borrower demand as lending rates go up. However, such challenging times also occur when real innovation tends to emerge. In APAC, we are seeing a shift in how lenders operate. Many are adopting AI and machine learning to improve their ability to assess risk and identify the most creditworthy borrowers. Digital lending journeys are becoming faster and more seamless, helping lenders approve loans more efficiently. Some lenders are also embracing risk-based pricing, which allows them to serve higher-risk borrowers while still protecting margins. Where regulations permit, alternate data is being used to expand credit access. What stands out is that the most agile players are leveraging technology to stay ahead, using flexible credit BRE systems that allow them to respond quickly to market changes. The common thread is that AI is no longer just an enabler; it is becoming central to lending strategy in this new normal.

How does Scienaptic leverage alternative data sources to improve credit access for underbanked and new-to-credit segments?

In India, Scienaptic has been a pioneer in using alternate data to unlock credit for underserved and new-to-credit borrowers. While traditional credit bureau data still plays a role, we’ve gone further by using insights from banking transactions, telecom usage patterns, utility payments, and even device-level signals. With the Account Aggregator framework, for example, we are able to assess income consistency, savings patterns, and financial resilience, even when a borrower has no formal credit history. Our AI models are built to identify positive behavioral traits that are often missed by conventional systems. This has made a significant difference in bringing first-time borrowers into the financial mainstream, particularly in consumer and MSME segments.

Scienaptic has onboarded over 150 financial institutions worldwide. What does the APAC expansion strategy look like over the next 12–18 months?

We are seeing strong traction in India, where Scienaptic has become a recognized name in underwriting technology. Our current focus is on serving mid-to-large NBFCs and well-capitalized fintechs that cater to consumers and MSMEs. We’ve also added a few banks to our client base and are keen to grow in the private-sector banking space. Southeast Asia is emerging as our next growth frontier. While adoption of AI-based underwriting has historically been slower there, that is starting to change. Real-time credit decisioning is gaining interest, and the regulatory environment is evolving in a favorable direction. To support this, we’ve broadened our product suite to include pre-built scorecards, lifecycle solutions like early warning systems, cross-sell triggers, and pre-approval modules. Our goal is to bring intelligent decisioning not just at the point of onboarding, but across the entire borrower journey.

From your vantage point, are regulators in Asia ahead or behind the curve when it comes to setting guardrails for AI in credit underwriting? What best practices should they adopt?

AI in lending is still relatively new in most parts of Asia, so it’s too early to make a definitive judgment. What is clear is that fintechs have led the adoption of AI while larger banks have moved more cautiously. Regulators are trying to keep up, often focusing on curbing malpractice without creating unnecessary roadblocks for innovation. The best path forward is one that protects consumers while still enabling experimentation. A few best practices would be to encourage transparency in AI decisions, promote explainability and fairness in models, and support collaboration between fintechs, banks, and data providers. A light but forward-looking regulatory touch can go a long way in ensuring that AI is used responsibly without slowing down its positive impact on credit inclusion.

How is Scienaptic working with mid-sized or smaller institutions such as NBFCs and Fintechs to enhance their digital lending capabilities?

Smaller and mid-sized lenders often face limitations in terms of resources and technical expertise. Our platform is designed to bridge this gap. It is easy to deploy and comes with pre-trained models that are already tuned for high accuracy. Lenders can get started quickly and begin seeing results without a long onboarding process. For NBFCs and Fintechs, we provide a user-friendly BRE (business rule engine) layer that integrates with their existing origination systems. This allows them to automate underwriting, test and learn faster, and reduce turnaround times without needing large in-house data science teams. As a result, these institutions can compete with larger players while continuing to serve their local communities effectively.

What role can companies like Scienaptic play in driving the adoption of a standardized and interoperable credit identity system?

Scienaptic can play an important role by building the infrastructure that allows data to be connected, interpreted, and shared securely across different lenders. Today, a borrower’s financial footprint is fragmented across banks, bureaus, fintechs, and digital platforms. By connecting to multiple sources of data and enabling intelligent decisioning on top of it, we can help create a unified view of credit identity. We are also advocating for industry-wide standards and participating in regulatory discussions around frameworks like the Unified Lending Identity. As a neutral platform with experience across markets, we can help bring together banks, fintechs, and public infrastructure in a way that benefits the entire ecosystem.

How does your platform dynamically recalibrate risk models in response to macroeconomic shifts, such as rising interest rates or inflation-driven behavioral changes?

Our platform continuously adapts to changing conditions by learning from real-time data. The models are designed to update themselves based on new borrower behavior and loan outcomes. For example, if there is a shift in repayment trends due to inflation or job market changes, the platform picks up those signals and adjusts approval thresholds or model weights accordingly. We also provide early warning indicators and performance dashboards that allow lenders to take proactive measures. Business users can make policy changes quickly using our rule engine (BRE), ensuring that lenders stay in control even as market conditions fluctuate. This combination of automation and human oversight is essential to stay resilient in uncertain times.

Co-lending models are gaining ground in Asia. How does Scienaptic support multi-party credit decisioning frameworks and shared risk intelligence among partners?

Scienaptic is built to support complex credit ecosystems, including co-lending models where multiple institutions participate in the same transaction. One of our key innovations is the lender recommendation engine, which evaluates each borrower profile in real-time and suggests the most suitable lending partner based on parameters like credit appetite, cost of funds, pricing expectations, and commission structures. The goal is to maximize the total economic benefit for all parties involved. Our platform also enables shared underwriting logic and transparent risk scoring across partners, which helps ensure consistency and trust in the decisioning process. This has made our platform particularly attractive to fintechs and NBFCs operating in partnership-driven lending models.

You’ve proposed the Unified Lending Identity (ULI) as the UPI-equivalent for credit. What governance models or stakeholder incentives would be necessary to make ULI adoption feasible across public and private sector players?

The success of ULI will depend on having a governance model that is neutral, trusted, and inclusive. Ideally, this would be led by a public-private consortium with participation from regulators, banks, fintechs, credit bureaus, and technology providers. Borrowers must have full control over their data, with clear consent frameworks in place. Stakeholders should be incentivised to contribute data through access to shared intelligence and reduced risk. Just as UPI made payments universal and instant, ULI has the potential to make credit more accessible and fair by ensuring that a borrower’s data moves with them across institutions. We believe this kind of infrastructure is the next big leap for financial inclusion.

There’s been a surge of VC interest in vertical AI for fintech. How should startups distinguish between genuine product-market fit and temporarily inflated interest in AI-led narratives?

The real test of product-market fit is whether clients are using your product in production, seeing value, and expanding their usage. If the business grows without relying on deep discounts or pilot-driven hype, that is a good sign. AI is a powerful tool, but lenders are becoming cautious about black-box solutions that cannot deliver explainability or outcomes. Startups need to focus on solving real-world problems, showing tangible results, and being transparent with their capabilities. While investor interest may come and go, sustainable adoption only comes from consistent delivery and trust and creating incremental bottom-line impact for clients.

Do you believe the future of lending will be fully autonomous—with real-time, API-triggered credit decisions—or will human credit committees always have a role to play for high-ticket or SME loans?

Lending will evolve as a combination of both. For small-ticket, high-volume consumer loans, full automation is already becoming the norm. The unit economics demand speed and efficiency, and AI is well suited for that. On the other hand, for large-ticket or SME loans where there is more complexity, collateral, or negotiation involved, human judgment will continue to play a key role. However, even in these cases, AI can support decision-makers with smart insights, simulations, and red flags. The future lies in blending the strengths of technology with the experience and discretion of human credit officers.

As artificial intelligence becomes an indispensable pillar of financial innovation, companies like Scienaptic are leading the charge in democratizing access to credit and transforming risk decisioning across Asia-Pacific. Scienaptic is helping financial institutions stay resilient, agile, and forward-looking by combining advanced AI models with actionable insights and inclusive data strategies. With a strong commitment to responsible AI and scalable impact, the company’s journey underscores how technology, when applied thoughtfully, can build a more inclusive and efficient financial future.