India’s financial industry is transforming, with artificial intelligence (AI) emerging as the catalyst for change. It is utilised to personalise customer services, streamline operations efficiently, and offer proactive solutions by analysing data. However, AI covers a crucial aspect of fraud detection and enhancing security measures.

According to data from banks and NBFCs, in 2023 alone, the volume of financial fraud in India reached 19.94 lakh, which amounted to INR 2,537.35 crore. AI revolutionises fraud detection in banking by enabling real-time monitoring, anomaly detection, and machine learning, allowing banks to develop more robust security measures against fraud and scams.

Adopting AI is no longer a luxury but a necessity for financial institutions to bolster security measures. Failing to embrace AI technology in this rapidly changing landscape could expose these institutions to heightened cybersecurity risks, economic losses, and regulatory non-compliance.

AI makes a difference.

By leveraging AI, financial institutions can enhance risk management practices in several ways. AI enables the development of sophisticated risk models by analysing vast amounts of historical data, market trends, and other relevant factors, providing more accurate risk assessments. It prevents them from facing unwarranted financial losses through fraud. A 2022 study by Juniper Research found that AI-driven fraud detection systems could save banks approximately US$ 10 billion annually.

With the rapid digitisation of financial transactions, the need for robust fraud detection systems has never been greater. In India, AI technology will play a vital role in risk management and fraud detection within the financial services sector.

Risk Management: AI empowers the creation of advanced risk models by harnessing its capability to analyse extensive historical data, track market trends, and consider other pertinent factors. This translates into more precise risk assessments within the financial sector. Notably, Madhabi Puri Buch, the Chairperson of SEBI, has underscored AI’s pivotal role in revolutionising risk management and elevating regulatory compliance in the industry. She emphasised that AI allows for tailored regulations aligned with each organisation’s risk profile. Effective risk management reduces regulatory burdens, with AI facilitating the transition from a broad risk-based framework to more precise, customised regulations. This evolution simplifies compliance for all regulated entities. AI’s data-driven insights and predictive capabilities are crucial in identifying potential risks early, enabling financial institutions and regulatory bodies to address and mitigate these challenges proactively. This integration of AI in risk management is instrumental in enhancing the overall stability and security of the financial industry.

Fraud detection: AI algorithms swiftly analyse vast transaction data, flagging unusual patterns and deviations and even identifying previously unseen fraud schemes. This transformative technology enables banks to swiftly identify unusual patterns, suspicious transactions, and potential fraudsters, mitigating financial risks and safeguarding customer assets–enabling multiple banks to incorporate security systems integrated by AI. For instance, HSBC uses AI-based robots to help spot money laundering and detect fraud. AI’s ability to continuously adapt and learn from data ensures that fraud detection systems become increasingly influential, staying one step ahead of ever-evolving fraudulent tactics.

KYC Enhancements: AI is reshaping the Know-Your-Customer (KYC) landscape in India, making it more efficient, secure, and customer-centric. AI technologies, such as machine learning and natural language processing, are being employed to streamline KYC procedures by automating identity verification, document authentication, and risk assessment. This not only improves the efficiency and accuracy of KYC checks but also helps in compliance with regulatory requirements. Even the Reserve Bank of India (RBI) has issued guidelines and directions to adopt AI technology for KYC enhancements, emphasising the importance of this technology in the financial sector.

Reducing False Positives: Traditional fraud detection systems frequently produce false positives, triggering unnecessary investigations and consuming valuable time and resources. For instance, in loan approvals, a false positive occurs when a non-creditworthy applicant is mistakenly approved for a loan, potentially leading to higher credit risk for the lender. AI addresses such issues effectively by continuously refining its detection criteria. AI algorithms learn from patterns and anomalies, allowing them to adapt and reduce false alarms over time. This enhances fraud detection accuracy and optimises operational efficiency within financial institutions.

Regulatory compliance: Banks & Financial institutions undergoing significant changes must comply with emerging laws around digital innovation. Regulatory agencies understand the necessity for new safeguards and rules to be reshaped by AI. New approaches can change the risk profile of banks for better or worse.

Cybercrime prevention: Financial institutions and regulatory authorities are increasingly becoming innovators rather than adopters of AI/data techniques.

Based on standard criteria, AI algorithms can accurately identify money laundering attempts. For instance, MasterCard reduced fraud by 50 per cent this way. Banks can play a proactive role in protecting the public during these strange times.

Challenges in Adoption

According to joint research conducted by the National Business Research Institute and Narrative Science, about 32% of financial service providers in India are already using AI technologies such as voice recognition and predictive analytics. Banks such as BoB, SBI, ICICI, HDFC, Yes Bank, and others already deploy artificial intelligence to streamline their day-to-day processes. According to an Accenture banking technology vision report, 83% of Indian bankers believe AI will work alongside humans in the next few years. However, the report also noted that 77% of Indian bankers agreed that they have to develop and implement AI tools in banking services.

The adoption of AI in the Indian banking system can be further enhanced by addressing the following challenges:

1. Trained workforce: Due to the unavailability of professionals with requisite data science skills and trained human resources, the banking industry needs to work with the top Indian universities to develop skilled data scientists.

2. Data standardisation: Non-uniform digitisation of data will lead to issues on interoperability of the individual services and their usability.

3. Different approaches of enforcement: Differing enforcement approaches make it hard for firms to adopt practical global standards and quantify their risk of rolling out AI innovations internationally.

4. Data protection and privacy: Systems require vast training data as inputs. Consumer data is continuously collected by tracking online and offline consumer behaviour to generate big data sets and extract further information about consumers through profiling. This creates a massive risk to data privacy.

5. Languages: Given the diversity of languages in India, it becomes a challenge to create a system to enable communication services in vernacular languages while simultaneously training the machine to read the same

6. Regulatory Framework- The current law infrastructure is at a nascent stage and does not address the implications of:

● biased data outcomes
● sharing of sensitive or personal data/information
● not taking responsibility in the case of an accident involving human/property losses
● AI model and outcome transparency
● AI robots and their eventual rights (citizenship, licenses, etc.)
● intellectual property rights (IPRs), copyrights, competition laws, and patent credits, among others

AI is a necessity

To conclude, As we look ahead, the role of AI in preventing frauds and scams within financial institutions is poised for significant growth and development.

This transformation has primarily been driven by the proliferation of digital banking initiatives – which has helped to generate data that banks can use to monitor, predict, and respond better, thus opening opportunities for AI and analytics implementation in banking. Data-driven decision-making use of AI and analytics can help banks across various functions to improve overall customer experience, make more informed decisions on credit underwriting, detect frauds and defaults early, improve collections, and increase employee efficiency.

With the support of government initiatives to bolster cybersecurity and financial sector resilience, AI-powered solutions will continue to evolve. These innovations will enable financial institutions to detect and thwart fraudulent activities more effectively and foster greater customer trust by reducing false positives and minimising disruptions to legitimate transactions. The synergy between advanced AI technologies, regulatory frameworks, and public-private partnerships promises a future where financial fraud prevention becomes a defensive measure and a proactive and adaptive safeguard, ensuring the security and integrity of economic systems for all stakeholders. However, some challenges still need to be addressed to increase the adoption in the Indian banking and finance industry.

Views expressed by Chinmoy Banerjee, Global Head – Banking at Hexaware Technologies