Artificial intelligence (AI) and machine learning (ML) have transformed the way the Banking, Financial Services, and Insurance (BFSI) sector operates. If you are part of this sector, you already know how complex, data-driven, and highly regulated the environment is. That is where AI & ML consulting comes into play. It helps FinTech bridge the gap between traditional processes and intelligent automation. But applying these technologies requires more than just software. Companies need the right strategies, datasets, compliance knowledge, and operational readiness.
Let us explore a few use cases where AI and ML in BFSI consulting add value.
Key Use Cases of AI & ML Consulting in the BFSI Sector
Here is how AI and ML in BFSI software consulting can support banking and financial institutions:
Fraud Detection in Insurance
Fraud in insurance claims remains one of the most significant drains on profitability. With AI & ML consulting, insurers can deploy supervised and unsupervised learning models to flag anomalies in claims processing. For example, consultants help train models that compare claim patterns to historical data and detect outliers based on location, time of claim, behaviour of the claimant, or even the repair invoice details.
Advanced use cases include natural language processing (NLP) that reads textual descriptions submitted with claims and evaluates consistency.
Risk-Based Lending
When evaluating loan applications, relying solely on credit scores and income verification may not provide a complete picture. AI consulting allows FinTech to design ML models that assess risk more holistically. These models consider alternative data such as payment history of utility bills, spending patterns, transactional behaviour, and even social signals.
By building risk profiles that are more dynamic and context-specific, FinTech companies can make lending decisions that are fairer and more accurate. AI consultants also help maintain model fairness by detecting bias in training data, which is especially important when underwriting loans for underserved or new-to-credit segments.
Personalised Wealth Advisory
For wealth management providers, offering personalised recommendations to thousands of customers can be challenging. AI & ML consultants help design advisory engines that analyse a customer’s financial goals, current portfolio, income sources, risk appetite, and even life events to generate tailored investment strategies.
These systems use reinforcement learning to improve over time based on customer responses. Consultants also ensure that the algorithms remain compliant with regulatory frameworks around investor suitability and product disclosures.
Attrition Prediction
Customer retention is more affordable than bringing in new customers. With the help of AI & ML consulting, businesses can predict customer churn by analysing patterns such as reduced account activity, missed EMI payments, fewer product logins, or support complaints.
Consultants also guide in collecting the right behavioural data, cleaning it, and applying classification models to segment at-risk customers. By identifying who is likely to leave and understanding the reasons behind their departure, companies can implement targeted retention strategies, such as offering special incentives, bundling products, or improving relationship management.
Compliance Monitoring
Financial institutions operate under heavy compliance obligations. Whether it is anti-money laundering (AML), FATCA, or Know Your Customer (KYC) norms, non-compliance can lead to serious penalties. AI consulting helps implement systems that automatically monitor transactions, generate alerts for suspicious activities, and audit data trails in real-time.
ML models detect deviations from expected behaviour, such as unusually high remittance volumes or transactions from flagged geographies. AI experts also integrate tools like a CKYC Solution to streamline and standardise customer verification processes across platforms.
Insurance Underwriting
Manual underwriting is not only time-consuming but also leads to inconsistent decisions. AI & ML consulting allows insurers to automate underwriting by training models on historical approval data, medical records, claim history, and risk indicators.
These systems can read unstructured documents, identify key attributes, and classify risk with a high degree of accuracy. Consultants also help structure data pipelines, integrate third-party data, and build explainability layers so that underwriters and auditors can understand how decisions are made.
Chatbot Optimisation
Many BFSI firms have already deployed chatbots, but the real challenge lies in making them smart, context-aware, and multilingual. AI & ML consulting helps them improve chatbot design by training natural language understanding (NLU) modules on domain-specific queries and regional languages.
Consultants guide them in managing intents, dialogues, escalation protocols, and feedback loops. The result is a more human-like conversational experience that resolves queries more quickly and improves overall satisfaction. Well-designed chatbots also reduce operational load on the call centres.
Credit Scoring for MSMEs
Most traditional credit scoring models fail to evaluate Micro, Small, and Medium Enterprises (MSMEs) effectively due to their limited financial documentation. Through AI & ML consulting, banks can build dynamic credit scoring systems that analyse alternative indicators such as GST filings, supply chain records, account transactions, and invoicing cycles.
Consultants also help identify which data points are reliable predictors of creditworthiness. They also guide the setting up of periodic model retraining, allowing the system to adapt as the business evolves. This allows FinTech companies to offer customised credit products to MSMEs with reduced risk.
Conclusion
AI and ML consulting is helping the BFSI sector solve complex problems with smarter, data-driven solutions. From fraud detection and risk-based lending to chatbot optimisation and compliance monitoring, each use case shows how technology can enhance efficiency and accuracy. With expert guidance, financial institutions can better serve their customers, manage risks effectively, and remain compliant. These tools are not just upgrades; they are important for growth, innovation, and staying ahead in a rapidly changing financial ecosystem.