The Reserve Bank of India (RBI) mandates that all regulated entities in the BFSI sector adhere to strict guidelines for risk management, customer satisfaction, and fraud prevention. Many of Inspira Enterprise’s clients in the BFSI sector faced challenges in aligning with RBI’s regulatory framework.
As a leading Data Analytics and AI services provider, Inspira delivered tailored solutions that empowered banks and financial institutions to achieve regulatory compliance, operational efficiency, and improved customer satisfaction.
Inspira’s Core Strengths
The core team combines deep technical expertise with hands-on banking experience. It includes technology specialists with a banking background and banking professionals experienced in tech implementations, ensuring solutions are both robust and industry-aligned.
The team brings a wealth of experience in Public Sector Banks (PSBs) across diverse tracks, from risk, compliance, and governance to business growth drivers, with data warehousing and data lake solutions at the center of its approach.
Inspira’s dedicated R&D lab fuels its continuous pursuit of technological advancement. Here, its team works to maximize the capabilities of the tools it implements—such as SAS, among others—in the BFSI sector, continually upgrading them to meet evolving market demands.
Inspira views discipline as a core practice. The team actively assesses regulatory updates and emerging business challenges across the BFSI landscape. It transforms these insights into actionable strategies, enhancing its solution design and consistently upgrading services for its clients.
A dual-pronged IT transformation landscape primarily shapes the BFSI sector. On one side, Lines of Defense (LOD) oversees Risk, Audit, and Compliance functions, ensuring regulatory alignment and risk mitigation. On the other, the Revenue Generators or Direct Business Impact track encompasses wholesale, retail, third-party, and other business functions, driving growth and market expansion.
LOD Track
Everchanging and Dynamic Regulatory and Risk Management
The Reserve Bank of India (RBI) enforces strict compliance for regulated entities, including mandates on Fraud Risk Management, AML-KYC, Early Warning Systems (EWS) automation, and comprehensive risk management. Inspira has been a frontrunner in implementing Tech and RegTech solutions across several Public Sector Banks, keeping pace with evolving regulatory requirements. This requires robust, scalable, and agile data warehouses and data lakes. Key differentiators in Inspira’s solutions include tailored use cases that support REs (Regulated Entities) in adhering to RBI’s dynamic policy framework.
Fraud Detection and Prevention
Real-time fraud detection and prevention were unattainable, requiring long-term support for specialized software, including optimization, upgrades, and sizing. Expertise in money laundering, asset management, compliance, and robust fraud management was also essential.
Solution:
Inspira established a robust fraud detection system leveraging AI and machine learning to monitor transactions in real-time, identifying anomalies and potential fraud risks.
o Real-time anomaly detection:
Unusual patterns in transaction data to detect fraudulent activities were identified helping the Banks and Financial Institutions (Fis) to prevent losses and create a customer-centric payment ecosystem.
o Behavioral analytics:
Customer behavior to detect signs of fraud or identity theft was analyzed. There has been a thrust by the regulator to make the Banks and FIs more proactive in prevention through various modern ML techniques and Inspira has helped the Banks to achieve the same.
o EWS –
An Early Warning Signal (EWS) system was implemented to enable proactive detection and prevention of Red Flagged Accounts (RFA) and fraud. The system utilizes workflow-driven credit and performance monitoring triggers, enhanced with AI/ML-driven capabilities for automatic detection and processing
• Regulatory Compliance
(Inspira to add the challenge)
o Know Your Customer (KYC) verification:
AI was leveraged to automate KYC verification processes.
o Anti-money laundering (AML) detection:
Suspicious activities to comply with AML regulations were identified.
Risk Management
RBI guidelines require banks to adopt advanced methodologies for Credit, Market, and Operational Risk management, including the Internal Capital Adequacy Assessment Process (ICAAP) and Asset Liability Management, in line with Basel II and III standards.
Solution:
o BASEL 2/3 standards and advanced methodologies for managing credit risk, market risk, asset-liability management, operational risk, and RCSA (Risk and Control Self-Assessment) automation were implemented. This comprehensive solution supported an enterprise-integrated risk management framework.
o Predictive Modelling for Risk Management – The probability of Default, RFA and Fraud – ML models using various machine learning techniques and MLOps helped Banks and FIs to arrest the slippages and save on default and losses.
Predictive Analytics for Credit Deterioration
Early detection of credit deterioration is crucial for risk management, yet traditional methods that rely on transaction data alone were used, missing broader industry correlations. Manual assessments further delayed timely, accurate credit evaluations.
Solution:
Inspira implemented predictive models to detect early signs of credit deterioration using various analytics tools. These tools developed and regularly evaluated quantitative and qualitative early warning indicators. This comprehensive monitoring framework enabled quick identification of credit risk across the entire portfolio, as well as within sub-portfolios, industries, regions, and individual exposures.
Direct Business Impact Track
In today’s rapidly evolving, technology-driven financial ecosystem, banks and FIs face constant pressure to stay competitive against agile, tech-driven financial product companies leveraging AI, ML, and Big Data. Recognizing these industry shifts, Inspira empowers banks and FIs to stay ahead of the curve with cutting-edge, tech-enabled business generation solutions. The following use cases have been implemented by Inspira across the industry to meet these demands.
Customer Segmentation and Customer Service
Customer data spread across multiple systems hindered a holistic view, limiting accurate segmentation and targeting. Meeting expectations for immediate responses and delivering personalized service at scale remained a major challenge.
Solution:
Inspira developed detailed customer profiles using demographics, behavior, and preferences to create targeted marketing campaigns. Customer churn prediction identified at-risk customers for proactive retention strategies. Chatbots and virtual assistants provided 24/7 support, while sentiment analysis assessed feedback to identify improvement areas and enhance service quality.
o Customer profiling:
Detailed customer profiles based on demographics, behavior, and preferences were created. The team cut across large data sets with legacy overruns and created transformed and cleansed marts, which had been visualized to create interactive dashboards, resulting in strategic insights delivered to all key stakeholders.
o Targeted marketing:
Personalized marketing campaigns to reach specific customer segments were developed. The Inspira team created top-level clustering through various ML-driven techniques and RFM metrics to create an insightful data visualization for the banks and Fis.
o Customer churn prediction:
Customers at risk of churning were identified and proactive steps were taken to retain them. Inspira helped banks and FIs by leveraging ML, where they could gain a competitive edge by effectively managing customer churn, fostering long-term relationships, and driving business growth.
o Data-Driven Credit Offering:
Big data analytics coupled with ML models were implemented to assess the customer transaction, psychometric, behavioral, and financial conduct and preferences. On top of these, the clustering through various models and creating ‘offer buckets’ was a one-of-a-kind implementation done by the team.
Customer Service
o Chatbots and virtual assistants:
24/7 customer support to answer common inquiries was provided.
o Sentiment analysis:
Customer feedback to identify areas for improvement, Credit Scoring across wholesale and retail client bases, Credit Limit Optimization for credit cards, and Client Onboarding were analyzed.
Credit Scoring
Banks faced challenges such as opaque assessment methods, manual biases, and insufficient focus on macroeconomic and behavioral factors. Improving credit evaluation accuracy was key to reducing default rates.
Solution:
Inspira revamped credit scoring by leveraging predictive analytics to incorporate factors like spending habits, debt-to-income ratios, and economic conditions. The loan processing system autonomously approved or rejected proposals based on credit scores using analytics and machine learning. Key data sources included historical risk scores, sanctions, repayment histories, credit lifecycle data, industry specifics, default probabilities, governmental policies, and macroeconomic factors. The solution utilized risk approximation, deep learning, classification algorithms, K-Means clustering, and fuzzy logic screening.
Credit Limit Optimization
Banks faced challenges in setting optimal credit limits and managing risks while ensuring compliance with credit exposure norms. Manual assessments of borrower operations and collateral, along with approvals from high-level committees, slowed efficiency due to limited automation in risk management.
Solution:
Inspira implemented a credit limit optimization model aligned with RBI’s risk management mandates. Data was sourced from projected annual turnover, withdrawal frequency, existing loans, industry, operational area, collateral, and transactional data. Analyzing historical credit data, machine learning algorithms predicted credit limits for clients likely to utilize their approved capital, resulting in reduced limits for those less likely to use their sanctioned amounts.
