Speed alone cannot define digital lending in an AI-driven era: Vishal Bhati, Credit4Sure

While unsecured credit markets are changing at a rapid pace, digital platforms are increasingly looking towards AI as a means of speeding up their operations, with a more fundamental question arising: will speed and responsible lending coexist?

In an exclusive conversation with Express Computer, Vishal Bhati, Founder, Credit4Sure, a product of Mahavira Finlease Ltd., discusses his views on AI-driven lending, digital expansion, and the future of the credit world, where trust, data, and governance are just as important as speed.

AI in underwriting: Speed with accountability
AI-driven underwriting has revolutionised the process of unsecured loans, but speed, believes Bhati, must not come at the expense of judgement. “AI has certainly accelerated unsecured lending, but speed cannot replace judgement.”

At Credit4Sure, automation is seen as an enabler rather than a substitute for responsible decision-making. “We believe that automation should be improved, not replaced.”

This philosophy has been followed while developing the platform, as it creates its underwriting systems with clean data, transparent logic, and strong governance.

AI-led underwriting must be built on clean data, transparent scoring logic, and strong audit trails to meet regulatory expectations.
Human intervention still exists, especially in complex scenarios, as a validation of algorithmic results might be required.
“Human oversight remains important, especially in edge cases,” he says.

More importantly, Bhati stresses that sustainable lending depends on what happens beyond approval. “Fast approvals are valuable, but sustainable lending depends on accurate risk profiling and continuous model recalibration based on portfolio behaviour.”

Beyond credit scores: Understanding borrower intent
As borrowing patterns evolve from emergency credit to lifestyle-driven consumption, traditional credit assessment methods are no longer sufficient. “Lenders are not able to rely solely on traditional bureau scores anymore,” reminds Bhati.

In order to cope with the changing dynamics, fintech platforms are increasingly using alternative sources of information and analytics to develop a more comprehensive understanding of borrowers.

This includes analysing cash flows, interpreting bank statements, and categorising transactions in order to understand the repayment potential of borrowers in real time. “Machine learning models are also being used to identify patterns in spending behaviour, income stability, and credit utilisation, enabling more nuanced risk assessment. The focus is moving on to lending money responsibly,” adds Bhati.

This marks a broader shift in the industry, from transactional lending to behaviour-driven credit evaluation.

Building real-time lending systems at scale
Delivering fast, paperless lending experiences requires more than front-end innovation. It demands a robust backend architecture that can process, analyse, and act on data in real time.

At Credit4Sure, this is achieved through tightly integrated data engineering pipelines, AI models, and cloud infrastructure. “Our data engineering pipelines securely ingest bureau data, bank statements, and customer inputs in real time,” he says 

These pipelines enable structured risk assessment, while AI-driven models evaluate applications dynamically.

At the infrastructure level, cloud plays a critical role in ensuring scalability and performance. “Our cloud infrastructure ensures high availability and seamless performance, even during peak demand.”

However, Bhati is clear that speed cannot come at the cost of security or compliance.

To maintain trust and regulatory alignment, the platform incorporates strong encryption standards, controlled data access, and audit-ready processes. “Secure APIs, audit-ready processes and continuous monitoring allow us to deliver real-time decisions while maintaining governance, transparency and customer trust.”

Expanding into Bharat: Rethinking user experience
As digital lending expands into Tier II and Tier III markets, fintech platforms are being forced to rethink user experience from the ground up.

In these markets, accessibility, simplicity, and trust are more critical than feature richness. Their approach focuses on building mobile-first platforms that work effectively even in low-bandwidth environments. “Our mobile-first platform is built to function smoothly even in low-bandwidth environments.”

Equally important is vernacular accessibility and contextual guidance, enabling users to make informed decisions rather than just quick ones.

Trust-building remains central, especially in regions where digital finance adoption is still evolving. Transparent pricing, clear repayment schedules, and pre-disbursement verification processes are key components of this strategy. “Transparent pricing and pre-disbursement verification calls are integral to our process,” points out Bhati.

By combining digital KYC, assisted onboarding, and responsive support, the platform aims to make borrowing both accessible and reassuring.

The next phase: Predictive risk and personalised credit
Looking ahead, Bhati sees AI and machine learning evolving from reactive tools to predictive systems that anticipate risks before they materialise.

This includes advanced anomaly detection systems capable of identifying synthetic identities, device-level fraud, and unusual behavioural patterns even before loan disbursement.

At the same time, risk analytics is becoming more dynamic, moving away from static scoring models to systems that continuously adapt based on borrower behaviour.
“Dynamic scoring models will gradually replace static underwriting frameworks.”
Personalisation is expected to intensify, with credit products being even more closely aligned to individual income cycles and spending patterns.

“Credit limits and repayment structures are aligned to real income cycles,” he says. This is an indication of a move towards even more context-sensitive credit products, where they are aligned not just to individual profiles but to real-time behavioural patterns.

Building a sustainable credit ecosystem
Although technology is driving rapid innovation in the space, Bhati emphasises that the ultimate success of digital lending will be based on fundamentals, not speed.

“For fintech leaders, the priority must be ethical AI governance, explainable decision models, and full regulatory alignment.”
And as the ecosystem continues to evolve, considerations like data integrity, cybersecurity, and portfolio management will be critical differentiators.

“Long-term sustainability will depend on trust, data integrity, and disciplined portfolio management, not just rapid scale,” he avers.
This is a sentiment that is now being echoed throughout the industry, as it becomes increasingly recognised that the future of lending will not be determined by how quickly credit can be delivered, but by how responsibly it can be managed.