AI, GenAI, and blockchain: How technology is transforming BFSI sector

The Indian Banking, Financial Services, and Insurance (BFSI) industry has shown strong resilience amid global headwinds and achieved impressive growth. Credit growth has remained robust, and non-performing assets (NPAs) have reduced to multi-year lows. This progress results from significant technology investments to meet rising demand and support financial inclusion initiatives. BFSI firms across India are undergoing a major digital transformation, with technologies such as Cloud, AI, GenAI, Robotic Process Automation (RPA), and Blockchain leading the way. These technologies are reshaping the industry by enhancing customer experiences, streamlining operations, and ensuring regulatory compliance.

The surge in digital banking platforms and mobile apps has revolutionised customer interactions, delivering unprecedented convenience and accessibility. This article shares the findings of a survey-based research project that Everest Group conducted to understand the technical advances in the Indian BFSI sector.

The report provides an overview of technology trends in the Indian BFSI market; the regulatory landscape and its impact on BFSI firms’ technology investments; adoption maturity and drivers for various technology themes; and a deeper look at challenges and obstacles that Indian BFSI firms face as they expand their technology estate.

Enterprises looking to gain insights into current technology demands, future investment priorities, and the challenges facing Indian BFSI firms will benefit from this report.

India’s banking industry is experiencing remarkable growth driven by economic fundamentals, rising demand, and technological advancements. In FY24, total deposits surpassed Rs 200 trillion, with a 13 per cent year-on-year (Y-o-Y) growth, and net profits exceeded Rs 3 trillion, underscoring the sector’s resilience. Digital payments, especially through UPI, saw a significant rise, with 83.76 billion transactions in 2023, up 82.2 per cent Y-o-Y. Government initiatives like 75 Digital Banking Units and rural digitalization efforts further accelerate this transformation.

Technology, including AI/ML, Blockchain, and RPA, has boosted efficiency and customer experience. The Reserve Bank of India (RBI)’s Central Bank Digital Currency (CBDC) pilot aims to reduce costs and improve government securities trading. Digital banking now accounts for 92 per cent of financial transactions, bolstered by UPI-ATMs. Financial inclusion initiatives, such as PMJDY (Pradhan Mantri Jan Dhan Yojana), with over 51.11 crore accounts, and digital KCC (Kisan Credit Card) lending are expanding access to credit in rural areas, enhancing the rural economy’s financial connectivity.

This technology evolution of India’s BFSI sector is reshaping the industry, driving growth, efficiency, and inclusivity. Digital payment solutions, AI-based innovations, and government-led initiatives are not only expanding the sector but also improving customer experience, boosting operational efficiency, and strengthening regulatory compliance. Key technology trends transforming the industry include: AI/ML becoming integral to BFSI firms; eliminating manual tasks with RPA and hyper-automation; enhancing security and transparency with Blockchain and Distributed Ledger Technology; Cloud becoming the top choice for digital transformation; Regulatory Technology (RegTech) platforms and compliance automation gaining traction; investing in data and analytics to deliver hyper-personalised customer experiences; the rise of open banking and Application Programming Interface (API)-driven transformation in the BFSI industry; and the evolution of cybersecurity frameworks and advanced threat detection systems.

The Indian BFSI sector is navigating new and upcoming regulations aimed at transparency, consumer protection, cyber resilience, and financial stability. Approximately 35 per cent of respondents feel India’s regulatory environment is less stringent than that in North America, the UK, and Europe.

> Cyber Resilience and Digital Payment Security Controls (June 2023): RBI’s draft regulations emphasise real-time threat monitoring, Multi-Factor Authentication (MFA), and a zero-trust security model, prompting BFSI firms to upgrade cybersecurity with AI-powered fraud detection to protect customer data

> Payments Vision 2025: RBI prioritises NFC-based UPI Lite X, IoT-compatible payment systems, and biometric authentication, requiring BFSI firms to invest in IoT integration and AI-driven payment security

> Regulatory Framework for Web Aggregation of Loan Products (WALPs): BFSI firms must adopt API-driven platforms for transparent loan options and secure data sharing, necessitating robust data protection measures

> Securitisation of Stressed Assets: RBI’s guidelines (January 2023) encourage banks to use Blockchain for transparency in asset records and smart contracts for asset transfers, aiming to streamline NPA management

> Unified Regulatory Framework for Connected Lending: Expected in 2024, this framework mandates AI-driven due diligence and ML models for credit risk assessment across related entities, enhancing transparency in lending

> Transitioning to the Expected Credit Loss (ECL) Framework: Banks must adopt AI/ML-based predictive analytics and real-time risk monitoring to estimate credit losses accurately, enabling proactive risk management

> Guidelines for Digital Lending Platforms: Upcoming regulations stress data privacy, encryption, and transparency in loan disclosures, requiring digital lenders to enhance data security and transparent processing

These regulations collectively push BFSI firms to adopt advanced technologies—such as AI, Blockchain, and IoT—to meet compliance demands, improve operational resilience, and maintain customer trust amidst evolving regulatory standards.

Indian BFSI firms face significant challenges when expanding their technology estate, primarily due to legacy mainframe systems and integration issues. Legacy systems, built on outdated technologies like COBOL, are rigid and difficult to integrate with modern Cloud-native applications, microservices, and API-based solutions. These systems lack scalability and real-time data processing capabilities, which are essential in today’s data-driven environment. Integrating these systems with modern platforms requires complex middleware, introducing inefficiencies and increasing operational complexity. 

For instance, State Bank of India (SBI) relies on legacy core banking systems, challenging integration with advanced mobile banking, AI-based services, or Blockchain. Transitioning to microservices and API gateways is a solution, yet presents technical and financial challenges.

Cybersecurity and data privacy concerns intensify as BFSI firms digitise operations and migrate to Cloud platforms. Sophisticated cyberattacks, data breaches, and financial fraud become more frequent, and securing multi-Cloud and hybrid environments is complex. Implementing advanced frameworks like zero-trust security models and SIEM systems is resource-intensive. Compliance with India’s Data Protection Bill and General Data Protection Regulation (GDPR) imposes stringent data requirements. For instance, HDFC Bank experienced a data breach in 2022 due to unpatched vulnerabilities in digital payment infrastructure. Integrating AI-driven threat detection tools and behavioural biometrics strengthens cybersecurity defences but requires significant costs and expertise.

Scalability and Cloud migration issues are also prevalent as BFSI firms adopt Cloud models to enhance operations. Large-scale migrations introduce data sovereignty issues, multi-Cloud management challenges, and regulatory compliance concerns for sensitive financial data. A hybrid Cloud model requires intricate data governance for PII (Personally Identifiable Information) protection, and vendor lock-in complicates migration.

Axis Bank invested in Cloud infrastructure for digital banking, but compliance with RBI’s data localisation guidelines adds complexity. Containerisation technologies like Docker improve application portability across multi-Cloud setups, but managing container orchestration demands specialised skills.

Regulatory and compliance challenges increase as the Indian regulatory landscape becomes more stringent. Guidelines on data localisation, AML, KYC, and digital payments create roadblocks. This demands BFSI firms adopt RegTech solutions that leverage AI and Blockchain for real-time transaction monitoring, fraud detection, and automated compliance reporting. For instance, RBI’s data localisation mandates and the proposed Personal Data Protection Bill require banks to store critical data within India, limiting the use of global Cloud services. Blockchain and AI-powered RegTech solutions help with compliance, though high adoption and maintenance costs are challenging.

Talent shortage and skill gaps hinder BFSI firms’ expansion of technology capabilities, especially for AI/ML, cybersecurity, Cloud architecture, and data science. Training is time-intensive, and hiring skilled professionals is costly. Outsourcing technology needs to MSPs (Managed Service Providers) or partnering with fintech companies can bridge skill gaps but may impact IP control and innovation flexibility.

Data management and real-time analytics present ongoing challenges as BFSI firms generate massive amounts of data. Implementing big data infrastructure, such as Apache Kafka, is complex, and data silos complicate integration.

Here’s an example. HDFC Bank and SBI invested in real-time analytics platforms for personalised services but face challenges integrating legacy data. A data fabric architecture can unify disparate data sources but demands sophisticated setups and high investments. 

The Indian BFSI sector is advancing in adopting transformative technologies like AI, Cloud Computing, Blockchain, and IoT, with maturity levels varying across themes and business segments.

Cloud computing: Cloud maturity is high for non-core applications, but core system migration is still evolving. Banking institutions are gradually moving core functions to the Cloud to enhance scalability, cost efficiency, and data management, while mission-critical applications remain on private Clouds due to RBI’s data sovereignty requirements. ICICI Bank, for instance, uses a hybrid Cloud for compliance. Capital markets adopt Cloud cautiously, focusing on private Cloud for low-latency trading and risk analytics. The insurance sector increasingly uses multi-Cloud for policy management and claims, with SBI General Insurance leveraging a hybrid Cloud for scalability and compliance.

AI and ML: AI/ML adoption is widespread. Banks use AI for predictive analytics, fraud detection, and personalisation, with YES Bank applying real-time credit monitoring. In capital markets, AI supports high-frequency trading and quantitative analysis. Insurance firms use AI for underwriting, fraud detection, and damage assessment with computer vision models.

Blockchain and DLT:Blockchain is emerging, particularly in banking for trade finance and cross-border payments. Axis Bank uses Blockchain for remittances. In capital markets, Blockchain supports securities settlement and tokenisation. Insurance applications are nascent, with potential in claims processing and identity verification.

IoT:IoT is in early stages, notably in insurance, with Usage-Based Insurance (UBI) models, such as Bajaj Allianz’s telematics program for auto insurance. Banking’s IoT usage is limited but includes connected ATMs, while capital markets explore IoT for asset monitoring.

Security and data privacy:Security investments are crucial due to digitalisation. Banking adopts zero-trust and AI-driven threat detection. Capital markets focus on encryption, and insurance firms use SOAR (Security Orchestration, Automation, and Response) platforms for incident response.

RPA: RPA is mature, automating processes like KYC, transaction processing, and claims administration. ICICI Bank, for example, has deployed over 750 bots.

As these technologies evolve, they promise to enhance operational efficiency, scalability, and customer experience in the Indian BFSI sector.

Generative AI is making inroads into the Indian BFSI sector, with applications extending beyond customer service to impact back-office operations, such as code development, testing, and technical areas. These applications streamline IT, improve software quality, and reduce development time. Key use cases include automated code development and refactoring; automated testing and quality assurance (QA); AI-driven bug detection and fix suggestions; AI-driven security testing; automated documentation; synthetic data generation; and log analysis and incident detection; and code conversion and legacy modernisation.

However, GenAI brings challenges like data privacy risks, biases from training data, and operational disruptions. Barriers to adoption include data quality; legal and ethical complexities; technical integration with legacy systems; and talent shortages. A structured approach is essential for successful adoption.

To fully capitalise on the evolving landscape of GenAI, a well-defined, step-by-step implementation strategy is necessary. Everest Group has developed a nine-step framework to guide BFSI firms through the successful adoption of GenAI.

Everest Group’s nine-step adoption framework recommends: defining goals, identifying use cases, assessing RoI (return on investment), ensuring data readiness, evaluating legal and ethical factors, selecting providers, upskilling talent, initiating a PoC (proof of concept), and scaling deployment post-pilot. This step-by-step process helps BFSI firms capitalise on GenAI responsibly, addressing both operational needs and regulatory requirements, ensuring effective and responsible AI deployment in the Indian BFSI landscape.

The technological landscape of the Indian BFSI sector is evolving rapidly, driven by both the need for innovation and compliance with new regulations. This thought paper has explored the key technology trends reshaping the industry, including AI, Cloud Computing, Blockchain, and Automation.

Source: Business Standard