Basudev Banerjee Technology Advisor- Financial Services
Many discussions have focused on identifying key use cases in banking and financial services that drive efficiency, boost employee productivity, and deliver positive business results. These outcomes include increased revenue from new customer acquisitions, operational cost savings, or enhanced customer satisfaction and loyalty. Below are several critical and highly relevant use cases.
- Customer service
- Payments Reconciliation
- Credit behaviour analysis.
- Credit analysis for MSME/SMB sector.
- Credit analysis & reporting for large asset financing and credit decisioning.
- Customer onboarding and activation
- Knowledge Management and Process Optimization using Decision Tree
- Fraud & Risk management – AML & Suspicious Transaction Reporting
We will try to define a bit more on the scope of these use cases and how GenAI solutions can help.
1. Customer service
Optimized customer service is often the primary focus for GenAI deployment across industries. Despite advances, challenges remain in accessing comprehensive data due to information overload and unsynchronized systems. CRM tools can be useful, but multi-channel interactions increasingly require unified solutions that provide agents—both human and automated—with comprehensive customer insights. Since 2024, the integration of GenAI co-pilots has addressed this critical need, particularly within financial institutions.
2. Payments Reconciliation
With the rise of digital payments, banks and financial institutions face increasing challenges in payments reconciliation due to the volume and complexity of transactions. GenAI can help by identifying transaction patterns, classifying payments, tagging relevant details, and tracking sources and timing. Besides streamlining reconciliation, GenAI can also aid in fraud detection and prevention, safeguarding both banks and customers. Insurance companies encounter similar reconciliation issues with premium collections from various payment channels and are also turning to GenAI for solutions.
Areas where GenAI can help in the end-to-end payment process involves,
- KYC and customer validation
- Payments pattern definition.
- Fraud detection
- Payment routing
- Exception handling
- Payment data parsing and tagging
- Improved self service
- Intelligent chatbot for issue resolution and reporting
- Compliance & audit
Digital payments and receipts reconciliation has become a key challenge for Insurance companies as well. These companies are also looking at GenAI to help them with these payments arising out of premium collection coming through multiple payment modes.
3. Credit behaviour analysis.
As banks prioritise digital lending and retail assets, they face challenges in credit behaviour analysis and assessment, especially with increasing competition demanding faster decisions. GenAI can support this by detecting patterns across customer segments and asset types, improving credit decision processes.
Leveraging industry data, these insights can enhance credit scoring models by reducing any biases, provide clarity on emerging retail segments and industry perspective and help identify early signs of default and fraud.
4. Credit analysis for MSME/SMB sector
Credit analysis for small business and mostly in unorganized sector has been a major challenge over decades. Lack of structural data and risk analysis, these problems become a bottle neck to extend credit to unbanked and underbanked segments, which typically drives the SMB segment. In developing markets, SMBs generate about one-third of GDP, but their economic impact is often underreported because many are not included in tax or government records. This also creates lack of reference and historical data for credit analysis. GenAI with overarching coverage of industry, economy and population behaviour can help solving this analysis by bringing in external reference point.
A previous exercise provided insights on credit scoring and analysis for the SMB segment, sourced from one of the fastest growing ASEAN economies with a substantial SMB presence. The analysis indicated that SMBs located near large bank branches or government offices tend to have a lower risk of default, as their customer base often consists of individuals with fixed salaries, contributing to steady income—particularly in the F&B sector. Although this information was not previously incorporated into credit scoring models, the use of GenAI may enable more precise integration of such variables in future model development.
5. Credit analysis & reporting for large asset financing and credit decisioning
Assessing large credit exposures is challenging for banks due to factors like industry concentration and risks tied to major organizations. Banks must analyse competition, industry segments, and market trends, including tariff changes. GenAI streamlines this process by summarizing key risk parameters, offering a comprehensive view of credit exposure, and identifying possible mitigations.
6. Customer Onboarding and Account Activation
Customer onboarding with proper KYC and compliance is increasingly complex due to rising fraud and cyber threats. As a result, financial institutions are exercising greater caution during onboarding and activation to mitigate fraud risk and potential penalties related to AML and suspicious transactions. Generative AI, which integrates data from
various systems, is enabling financial institutions to verify onboarding and authenticate account activation more efficiently. Adoption of GenAI in this area has grown rapidly and is expected to continue as digital channels become the primary means of customer acquisition.
Instances of data fraud and impersonation continue to occur, even when customer validation processes include biometric keys and authentication methods. GenAI can easily detect these issues and block and report these accounts from activation even if they have been onboarded with validations.
7. Fraud & Risk management – Anti Money Laundering & Suspicious Transaction Reporting
Fraud risk management, encompassing both cybercrime and transactional fraud, is presenting significant financial, operational, and reputational risks for banks and insurance companies. Traditional rule-based systems for detecting and analysing fraudulent transactions are no longer able to provide comprehensive protection. Moreover, the burden of managing thousands of false positives resulting from the limitations of these systems has become a major challenge for large organisations. With heightened regulatory scrutiny and substantial penalties being imposed, banks and insurance companies have had no alternative but to adopt a ‘machine plus human’ approach for identifying, analysing, reporting, and mitigating suspicious transactions. However, this hybrid model has resulted in high operational costs.
Generative AI, with its advanced pattern-matching capabilities, historical trend analysis, and ability to align with governing rules and guidelines, offers an opportunity for banks and insurance companies to automate certain functions. This can reduce the need for manual intervention, thereby decreasing turnaround times (TAT) and associated costs. While human oversight will remain essential for processes such as Suspicious Transaction Report (STR) monitoring, GenAI solutions can significantly reduce the operational time and cost required to manage this critical function.
8. Knowledge Management and Process Optimization using Decision Tree
Knowledge management is essential for organisational growth, yet many companies struggle with inaccessible internal knowledge, which impacts customer service, product development, and operations. Without effective knowledge management and process mapping, even procurement can be delayed due to a lack of structured information. GenAI solutions are supporting financial organisations by streamlining the management of both internal and external knowledge in line with company regulations. Effective customer service relies on agents having access to organised information to resolve issues efficiently. Insurance companies have adopted GenAI-driven knowledge management linked to internal guidelines and decision trees, resulting in notable productivity improvements.
At DatalensAI, we believe financial institutions will increasingly focus on adopting these use cases to improve customer service, enhance operational efficiency, manage risk, and optimize service quality. Data preparation and engineering need attention before GenAI can deliver real business value. Future PoVs will cover these data topics in greater detail.
