By Jyothirlatha B., Chief Technology Officer, Godrej Capital
The conventional view of financial services as only transactional is rapidly evolving, thanks to Artificial Intelligence (AI). Financial institutions are evolving towards becoming more experiential and responsive. Generative AI (GenAI) is leading this evolution, redefining customer engagement, optimising risk assessment, and enhancing operational efficiency.
According to a survey by EY India in May 2024, 78% of financial institutions have already adopted or plan to adopt generative AI, and 61% expect a considerable increase in efficiency and decision-making.[1] AI is no longer hypothetical – it is actively reshaping essential functions, from customer relations and fraud prevention, to credit assessment and business efficiency.
However, while many organisations have initiated proof of concepts (PoCs), very few have successfully scaled these solutions into production environments. This gap highlights the need for a more structured and strategic approach to GenAI implementation and identification of potential AI distractions .
Transforming Customer Experience with Generative AI
Traditionally, customer service in financial institutions has been reactive. GenAI enables a shift towards proactive, hyper-personalised engagement. By analysing transactional history, service requests and customer preferences, AI-powered system offers tailored loan options, resolves grievances, and designs insurance policies with remarkable precision.
One of the most promising applications of GenAI is the creation of Customer 360 profiles. By analysing emails, service requests, and customer feedback, these profiles provide a comprehensive view of each customer, enabling service teams to offer highly personalised support.
Moreover, GenAI enhances multilingual communication, dynamically generating documents like loan sanction letters and welcome kits in the customer’s preferred language. This AI-driven accessibility ensures an inclusive, frictionless experience, breaking traditional barriers in financial services. For instance, GenAI enables real-time analysis of 100% of customer interactions – calls, emails, and Net Promoter Score (NPS) feedback – providing deep insights that enhance customer service and foster trust.
AI-Driven Credit Assessment and Risk Management
Risk management has always been central to financial institutions, but GenAI in financial institutions like Godrej Capital, is transforming it from a reactive function to a proactive one. Beyond mitigating losses, AI now helps anticipate and prevent them. With real-time database scans, pattern recognition, and anomaly detection, AI enhances fraud prevention by identifying suspicious transactions before they escalate into major risks.
GenAI is also reshaping credit risk assessment. Unlike traditional models, which rely on conventional financial data, AI incorporates alternative indicators such as utility bill payments and purchasing habits. This broader dataset enables lenders to build a more comprehensive credit history, improving risk evaluation and extending credit access to previously underserved customers.
In credit underwriting, GenAI takes risk management a step further. While machine learning has already improved credit scoring, GenAI integrates real-time data from sources like bank statements and credit bureaus, combining it with applicant details to generate a more accurate risk profile. This approach helps financial institutions detect red flags that conventional models might miss – whether it’s inconsistencies in transaction history or behavioural patterns that indicate future creditworthiness.
To ensure AI’s effectiveness, organisations must also establish a strong framework to identify the right use cases for implementation – balancing innovation with operational feasibility and business value.
By enabling more precise, data-driven lending decisions, GenAI not only minimises default risks but also expands financial inclusion. The result is a safer, more efficient lending ecosystem that benefits both institutions and customers.
Balancing Innovation with Responsibility
The Reserve Bank of India has estimated that GenAI, will add up to $438 billion to India’s gross domestic product (GDP) by the fiscal year 2029-30. But it has also raised concerns like data security breaches, algorithmic discrimination, and regulatory risks. The biggest concern is explainability. AI models, especially deep learning models, are usually black boxes, and need frequent audit of their decisions. Ensuring consistency and transparency in AI-powered financial decision-making will be key to building consumer confidence and regulatory sanction.
This requires robust AI policy and governance models to be in place – defining ethical standards, data handling practices, and escalation protocols.
Further, security risks related to financial system infrastructure are a big worry. AI-powered software needs to be protected from adversarial data tampering and spoofing attacks. Strong security measures must be foundational – to safeguard sensitive financial data.
Utilising strong governance frameworks, along with continuous monitoring and strong encryption practices, can help mitigate these risks.
Some of the AI security measures include:
* Advanced data encryption & anonymisation
* Multi-layered fraud prevention
* Regulatory compliance audits
* AI ethics training for employees
Additionally, a human-in-the-loop approach must be adopted for critical processes – especially in areas such as loan sanctioning or fraud detection, which ensures accountability, accuracy, and customer empathy.
By embedding these safeguards into AI deployments, financial institutions can foster customer trust while maintaining regulatory compliance.
The Road Ahead
Innovations will allow financial institutions to further enhance their customer engagement strategies, providing insights that were previously difficult or impossible to obtain. In an industry where customer loyalty and satisfaction are key differentiators, the ability to offer truly personalised, data-driven experiences will set forward-thinking financial institutions apart from the competition.
For financial institutions, GenAI is not only a way to improve productivity; it is a question of survival. GenAI is at the forefront of the future of financial sector, but its success depends on trust, transparency, and responsible implementation. Only those institutions that combine innovation with strong AI governance, use case clarity, robust security, and human oversight will succeed in moving from pilot to production – while truly transforming the financial ecosystem.