Bangaluru: Underlining the role and potential of AI in transforming India’s BFSI sector, the Institute for Development and Research in Banking Technology (IDRBT) released a whitepaper titled ‘AI in Banking: A Primer’ in association with Microsoft India today. The Institute for Development and Research in Banking Technology (IDRBT) is the premier Institute of Banking Technology in India. Established by Reserve Bank of India in 1996, the Institute spearheads efforts in providing state-of-the-art technologies for the Indian Banking and Financial Sector.
Aimed at supporting banks in their AI journey, IDRBT in association with Microsoft has worked out a framework and strategy for the successful adoption of AI for accelerated innovation and growth. IDRBT urges the BFSI organisations to increase focus on AI strategy, data management, internal digitization, talent creation and developing safe systems to improve their AI readiness. The white paper also introduces an AI maturity assessment model developed by Microsoft.
As India progresses in its economic journey, financial infrastructure will play a vital role in achieving India’s vision of an inclusive financial system. The Indian banking sector, which is already at the forefront of the fintech revolution, will be an integral part of this journey. The government has stated that for banks to transform and fulfil India’s growing needs, they must harness technologies such as AI and Big Data.
Shri Saurabh Mishra, Joint Secretary, Dept. of Financial Services, Ministry of Finance, Govt. of India released the White Paper, at an event organised by the IDRBT for the Board members of banks, in the presence of Microsoft India executives, on September 11, 2020 (Friday).
Speaking on the occasion,Dr. A. S. Ramasastri, Director, IDRBT said, In the coming years, Artificial Intelligence in Banking is expected to be as normal as using any office productivity tools. AI in combination with Cloud Computing, IoT, Blockchain, 5G and emerging technologies will increase customer experience and agility in product release. We are confident these technology collaborations will draw synergies across stakeholders.
Anant Maheshwari, President, Microsoft India said,
“The adoption of Data & AI has accelerated exponentially in the new normal, enabling organizations, individuals and governments to not only rebound stronger from the crisis but to reimagine a new future. The banking and financial services industry, a critical determinant of India’s economic success, has been at the heart of this change. Building a scalable, trusted model to leverage the full potential of data and AI will be central to driving meaningful innovation and digital transformation in the sector. This also has deep implications for financial inclusion and access. It is an honour for us to collaborate with IDRBT in the development of this paper and support its efforts to empower the BFSI industry in India to leapfrog into the future.”
Strategy for AI adoption in banking
While the sector is now increasingly adopting AI, according to AI in Banking,addressing the key challenges facing it today is critical. This includes the need for a trained workforce; lack of uniform data digitisation standards; varying enforcement approaches across countries; differing levels of digital literacy and capacity among users; and concerns around data protection & privacy. Multiplicity of vernacular languages is another factor that inhibits the effectiveness of AI in the Indian banking ecosystem.
The white paper recommends banks embrace IDRBT’s Data, Process, People and Technologyframework, developed with techno-bankers and analytical industry experts. It also stresses the urgent need for banks to assess their AI readiness using an AI maturity model and increase focus on:
· AI strategy: banks need to have a clear vision on what AI is to achieve; how they want to integrate it within their organization; feasibility and impact of investments and possible consequences on their internal dynamics.
· Data management: invest in the creation and storage of a large amounts of data to train the AI algorithms. Dividends yielded by AI are related to the quality and the quantity of the data recorded or stored.
· Internal digitization: digitize processes and operations, promote a pro-technology culture, and familiarize their employees with emerging technologies. It is important to educate them AI will complement and enhance their work and not replace them.
· Talent creation: develop and reskill their own talent pools in addition to hiring experts.
· Developing safe systems: banks need to increasingly invest in cybersecurity collaborations with technology firms to identify and plug potential threats.
A two-phase approach: The white paper suggests a two-phase approach viz., pre-adoption and during adoption for the effective development and implementation of AI tools. The pre-adoption stage would require familiarising the workforce with the required skills and a careful ROI assessment. During the adoption stage, banks and financial institutions must focus on making data secure, provide proper training to employees, and close the talent gap. It is also important to ensure that AI algorithms are ethically designed to avoid bias and are compliant with regulations to ensure fairness, accountability and transparency, and prevent incorrect decision-making.
AI in Banking also showcases successful use cases of public and private banks in the country that are already deploying the technology effectively. These include State Bank of India (SBI), Punjab National Bank (PNB), Bank of Baroda, ICICI Bank, HDFC Bank, and Citi Bank. According to the whitepaper, AI is demonstrating a huge impact for the early adopters at three fundamental levels: (i) the processes they adopt, (ii) their products and services and (iii) user experience. Predictive and ML-based analytics models are also helping banks increase revenue, reduce cost and improve risk profile by facilitating informed decisions on credit underwriting, detecting frauds and defaults early, improving collections, increasing employee efficiency and transparency.
