Optimising the Indian Banking industry using Artificial Intelligence

The banking sector has adopted data analytics and AI faster than most countries in India. According to a survey conducted by PwC-FICCI, AI applications are expected to help banks achieve potential savings of $447 billion by 2023.

In an exclusive chat with Journal of India AnalyticsSonali Kulkarni, lead, financial services, Accenture in India, shared her insights on the AI ​​world transforming the banking industry.

AIM: Banks have been using data and AI for some time. How has adoption been in India so far, and where is this headed?

Kulkarni: Despite a slow start, most mature banks in India have started their data and AI adoption journey by making fundamental investments in analytics use cases, data lakes and customer journey digitization. They are using data and AI to improve decision making across the banking value chain. For example, machine learning is being used to improve cross-selling efficiency, target digital marketing, estimate core balances, improve capital efficiency, and make smarter underwriting decisions.

During the pandemic, there has been a surge in investments in risk discovery and mitigation based on data and analytics to obtain early warnings of market and credit risk, predict liquidity needs, identify delinquency patterns, improve collection strategies, and also to detect fraud. AI is also being used to support the Know Your Customer (KYC) processes, generate credit appraisal memos and regulatory compliance tasks such as filing suspicious transaction reports.

We expect banks to continue innovating new cross-selling and profit optimization models to take advantage of their rich data reserves as well as getting better at building intelligent automation and AI platforms across the enterprise. The need to compete with digital native players, drive better customer experiences and get better value from digital investments will drive innovation in this space. Finally, responsible and explainable AI practices will be a key focus for banks.

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AIM: How can banks tackle AI-enabled business transformation to achieve higher returns on investments?

Kulkarni: First and foremost, AI should be implemented with a business intent rather than to fulfill a technological goal. Second, banks need to think about longer-term business outcomes and therefore need to go beyond proof of concepts and implement AI across the organization. Leadership commitment is key to achieving this.

Banks need to build an AI core that integrates core capabilities across the organization – such as cloud-native data lakes, AI services, data platforms and tools, security and robust governance. It is also vital that banks do not limit themselves to technological interventions but also focus on building data, digital and cloud computing skills in the workforce supplemented by new operating models, and data-driven ways of working.

AIM: How can banks use AI and analytics to unlock value in untapped segments like treasury operations and corporate banking?

Kulkarni: Treasury operations can benefit from curated AI algorithms that can analyze large amounts of data with greater accuracy, thereby helping treasuries manage risk and predict liquidity better. They can also manage cash more effectively and efficiently, and implement controls in a timely manner.

AI and analytics can play a central role in portfolio management and improving client servicing in corporate banking. AI-based insights, when applied to portfolio management on an ongoing basis, can enable proactive monitoring. They can also help the bank anticipate customer and service issues and empower relationship managers to take proactive or preventative steps. AI-driven prompts can help relationship managers prioritize key tasks.

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Ultimately, the real value that data and analytics can drive is in offering the bank a 360-degree view of the customer and breaking down the silos between the corporate and retail banking businesses. Data and analytics can address client needs across its entire ecosystem, including at the level of an organization (corporate banking), its suppliers (SMB banking) and its employees (retail banking). This approach would unlock value for customers and the bank as a whole.

OBJECTIVE: New entrants into the financial services sector may not have significant customer data to draw insights from. How can they start?

Kulkarni: Leveraging data is an ongoing journey, and every organization is at a different stage of this journey. New entrants to financial services without significant customer data can start by focusing on low-hanging results, such as heuristic analytics, where insights from available data are driven by business judgment or predictive insights from expert opinion. And as they accumulate more data, sophisticated analytics can be leveraged to replace expert judgment with machine learning models. They can also leverage partnerships to adapt customer insights, use bootstrapping techniques or proven pre-built analytical models, which have worked in environments with similar customer profiles or data sets.

AIM: How can Indian banks foster a more data-driven culture? What kind of infrastructure and skills do they need to invest in?

Kulkarni: Business leaders in banks must advocate that all decisions are supported with data-backed insights so that this approach is reflected in the bank’s processes and culture. This must be supported by a structured change management program that embeds the desired outcomes in normal banking processes.

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Banks must develop a comprehensive data strategy and make investments in an enterprise-wide data and analytics foundation, data governance and management processes. A key element is a modern data and analytics platform that identifies and coordinates contextual and connected data inside and outside the bank and can turn data into insights for easy consumption. This platform must be supported by enabling architecture such as cloud-based accelerators and self-service tools. When necessary, must be willing to migrate from legacy data systems to a scalable and modular data architecture, reconfigure processes and systems to support easy sharing of data across the organization.

Investing in data leadership is just as important as building data literacy throughout the organization. Banks need to build or hire for data, AI, analytical skills and related multi-disciplinary skills such as data visualization, data storytelling and behavioral sciences.

AIM: Experts say cloud adoption is key to banks becoming more data-driven. Can you elaborate?

Kulkarni: Limited data storage and computing power are making on-premises data lakes and analytics environments sluggish and expensive. The cloud can help overcome this challenge since it is scalable and offers elastic storage and computing. It allows banks to integrate data from different sources and make it more accessible in real time. This can enable agile reports, more sophisticated analytical models and insights for faster decision making.

Factors such as regulatory compliance related to data residency, customer data protection, security, and return on investments must be considered when planning a bank’s cloud computing journey.

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