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Artificial Intelligence: Revolutionizing Banking with Predictive Analytics
Ashutosh Uniyal Associate Vice President, Financial Services, ANZ | June 29, 2020
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Artificial intelligence (AI) is breaking new ground every day. So, it does not come as a surprise that AI is fast emerging as a crucial innovation driver in banking operations. It has been projected that by 2035, AI will boost the banking and financial services market by at least USD 1.2 trillion. AI is already playing an instrumental role in saving costs for banks, and it is expected to be worth USD 447 billion by 2023, with the majority of the cost savings coming from wide use of AI in front- and middle-office operations.

By 2035, AI will boost the banking and financial services market by at least USD1.2 trillion.

It is not only cost savings and value generation that AI will be a major part of, but it will be pivotal in reshaping the banking landscape. With the growing number of smart devices and mass proliferation of fast and uninterrupted internet-connectivity, customers are getting empowered to the extent that their expectations, too, are growing.

Banks and financial services providers are perpetually redefining their products to meet the ever-evolving customer expectations. This is where AI in banking is expected to make a definitive value addition by bringing predictive analytics capabilities to the table. In other words, AI-powered predictive analytics will enable banks and finserv enterprises to regularly revisit and rediscover their offerings, create suitable value propositions, and elevate customer experience (CX).

AI at the Heart of Digital Strategies

The relationship between financial institutions and their customers thrive on mutual trust and cooperation, and when it comes to money, far-reaching changes are never welcome. However, with the advent of emergent technologies such as AI and automation, coupled with the global need for digital transformation, banks have reached a point where future-facing, proactive changes are no longer value additions but are indeed key differentiators.

The competition is growing every day and to stay ahead of the curve, banking solutions must factor in customer needs even before they are spelled out. The conventional challenges faced by banks and their customers include long turnarounds, providing dynamic solutions, cybersecurity uncertainties, and oft-changing regulatory landscapes. But, the emergence of fintech solutions is aiding banks in their digital transformation initiatives while mitigating traditional banking challenges.

To bridge the gap between business objectives and client expectations, banks must devise and execute robust digital strategies that aim to optimize AI. One of the key objectives for banks is to personalize their solutions to best suit their customers, and with the amount of data being generated by customers on their smart devices, banks must deploy AI-enabled fintech solutions to develop usable customer insights. An overall analysis of client data available through the smart APIs as well as their social media entries, and e-commerce spending, provides banks greater clarity on the consumption patterns and spending habits of the customers.

This insight is the bedrock for banks to then design personalized offerings and services. For instance, customers spending a considerable amount of money on dine-outs and gas would require solutions such as credit cards with benefits aimed at spends on gas and restaurants. Similarly, predictive analytics can provide more actionable insights on customers who soon may need loans.

Additionally, AI in banking plays a crucial role in streamlining day-to-day operations, saving time and investment on infrastructure, and reducing risk. The other areas where AI-enabled fintech can enhance banking operations include workforce recruitment, credit score propensity modeling, fraud detection and prevention, personal finance management, hedging, customer valuation, and even strategic marketing.

Predictive Analytics and Compliance

The global rise in data malpractice and cybercrime, alongside the increasing popularity of open banking, has caused serious concerns among banks and financial institutions. As a result, regulatory bodies are perennially on their toes to prevent data leakage and ensure complete privacy. Banks are instructed to keep their cybersecurity initiatives updated with the help of watertight security measures, such as anti-money laundering and know-your-customer practices.

Data security worries have been further compounded by open banking regulations such as Payment Service Directive II (PSD2), which mandates banks to allow third-party service providers to access their IT infrastructure to design personalized products. In such a situation, banks and financial institutions are perpetually stuck in the conundrum of how secure is secure enough.

AI-powered predictive analytics steps up in such catch-22 situations and enables the banking industry to navigate the choppy waters. Smart algorithms scan all available data in the open banking ecosystem to check for discrepancies and suspicious transactions. The intelligent capabilities of AI are particularly useful in processing unstructured, third-party data to identify malicious transactions. Fluid data visualization helps organizations swiftly analyze complex data sets and recognize flaws that may have deceived human eyes. With AI playing a crucial role in enterprises ensuring know-your-customer and customer-due-diligence policies, the cost of compliance, too, reduces for banks and finserv companies.

As we transition to a new paradigm of digital banking and financial services, AI will be a crucial tool for the banking industry. It has the potential to not only increase the number of satisfied customers but also play a critical role for banks to achieve their long-term business objectives with ease. AI will transform the future of banking as we know it.