Non-performing assets (NPAs) have long plagued India’s banking and financial services sector. Traditional methods of credit risk management have often failed to detect early signs of borrower distress. As credit portfolios continue to grow in size and complexity, there’s also a critical need to consider automation and predictive intelligence, similar to many other sectors.
AI-driven early warning systems (EWS) have started to transform risk management in the banking, financial services, and insurance (BFSI) sector by automating monitoring and enabling proactive action before defaults occur, benefiting the borrower.
AI-Driven Financial Insights and Risk Management
In conversation with AIM, Jaya Vaidhyanathan, CEO of BCT Digital, a AI-based risk management company, said, “While traditional systems may flag individual anomalies, AI models excel at connecting the dots across seemingly unrelated data points to detect early signs of credit stress.”
Vaidhyanathan added that the data stream remains consistent when it comes to AI. However, an AI-driven EWS introduces a valuable layer of intelligence. This system not only processes large amounts of internal and external data but also identifies intricate patterns that are nearly impossible for humans to detect manually.
Tarun Wig, co-founder and CEO of Innefu Labs, told AIM that post-COVID-19, customer financial behaviour has shifted dramatically, marked by digital-first interactions, multiple income streams and new spending patterns. “AI bridges this gap by ingesting real-time, high-frequency data instead of relying solely on static financial statements or past repayment records.”
This enables early warning systems to continually learn and update risk profiles, identifying emerging stress signals much sooner than traditional models could.
He believes that AI systems can detect early distress signals by utilising market-specific indicators, such as currency fluctuations, geopolitical news, regulatory changes, and sector developments. For example, disruptions in supply chains or industry downturns can be identified through news feeds and social sentiment analysis. By integrating these unconventional data points with core financial information, AI provides a more comprehensive view of creditworthiness, especially in volatile markets.
Vaidhyanathan believes that banks are increasingly adopting streaming architectures while acknowledging their complexities. BCT Digital’s rt360 EWS is designed for flexibility, integrating both traditional methods, such as ETL, database links and flat files, and modern approaches, such as application programming interfaces (APIs) and streaming feeds.
BCT Digital has developed a Real-Time Monitoring System (RTMS) to enhance low-latency alerting. This system enables near real-time data ingestion through APIs, bots, and streaming pipelines, which is essential for timely alerts, she added.
The RTMS includes an expandable alert library for all bank portfolios with customisable thresholds. Moreover, it uses in-memory processing to detect suspicious transactions within milliseconds, facilitating immediate action and low-latency alerts.
Encora, a digital product and software engineering provider, believes AI and machine learning are significantly reshaping traditional credit risk models, especially as consumer behaviours shift following COVID-19.
Encora also partners with BFSI clients to develop scalable, AI-driven EWS and real-time data pipelines using cloud-native architectures. By leveraging industry-specific AI accelerators, we deliver explainable and regulatory-compliant models that are ready for production, ensuring effective and proactive decision-making, Chinmay Mhaskar, executive vice president at Encora, told AIM.
Speaking about a real-time instance of EWS deployment at a prominent public sector bank, Vaidhyanathan said that BCT Digital implemented additional scenarios specifically designed to detect mule accounts, given the rising threat of fraudulent financial activity. Within just three months of rollout, the system successfully identified over 8,000 mule accounts using real-time data patterns and behaviour analysis. These accounts were immediately flagged and frozen in real time, helping the bank prevent potential financial losses and regulatory breaches.
Encora uses AI to enhance customer insights and manage strategic risk effectively. Our solutions predict default and renewal risk using machine learning for behavioural modelling and by scoring churn based on policy and payment patterns.
Mhaskar highlighted that the company uses natural language processing (NLP) to analyse digital interactions and understand customer behaviour. By integrating credit risk and portfolio management, Encora turns default risks into measurable credit exposure. Its offerings include pre-trained AI models, real-time MLOps pipelines for risk scoring, and a unified view of risk by merging CX/UX data with policy histories, supported by API interoperability between credit and insurance systems.
Unstructured Data Struggles
The rise of digital banking and neo-banks presents new opportunities, alongside challenges related to data velocity and complexity that traditional systems struggle to manage. Mhaskar pushes for AI-powered EWS to address issues such as unstructured data, fragmented ecosystems, and the need for real-time analytics, while also grappling with limited historical data and persistent concerns about data quality.
Encora mitigates these challenges by developing AI-ready data mesh frameworks tailored to the fintech ecosystem and ensuring reliability through end-to-end MLOps orchestration. “We co-develop real-time, AI-ready data mesh frameworks tailored to the fintech ecosystem.
Our NLP and behavioural models extract insights from digital signals, such as frustration events or session drop-offs, including pre-built API connectors, thin-file credit scoring templates, and customisable EWS dashboards,” Mhaskar highlighted.
Similarly, the rt360-EWS is built to ingest structured, semi-structured, and unstructured data, converting them into a unified format for streamlined processing. Vaidhyanathan said financial institutions function within complex and non-standard IT ecosystems, which exhibit varying levels of data maturity. Therefore, they have implemented a diversified data ingestion strategy tailored to each specific data type and use case.
Tackling Other Challenges
Wig added that ensuring fairness begins with the curation of diverse data across different geographies and demographics. Regular bias audits and fairness-aware algorithms help identify and reduce discrimination. Moreover, transparent governance and human reviews are essential to prevent automated decisions from disproportionately impacting any community and maintain ethical AI practices.
Vaidhyanathan believes that transparency is crucial in regulated environments. BCT Digital’s EWS ensures that stakeholders understand the decision-making process by providing clear explanations for each alert and maintaining a detailed audit trail. “This transparency allows
credit officers to understand not just that an alert was raised, but why it was raised—building
confidence in the system’s output and enabling better decision-making.”
“AI-powered EWS offer transformative potential for risk management, but for traditional financial institutions, adoption comes with real-world complexities. Financial institutions aren’t lacking intent; they’re grappling with deeply entrenched barriers across people, process, and platform,” Mhaskar concluded.
The post Indian BFSI Reinvents Risk Detection with AI-Driven Early Warning Systems appeared first on Analytics India Magazine.
