AI in banking: balancing cost and security in the AI race

With generative AI taking the world by storm, banks across Asia Pacific are adopting GenAI in their product and service offerings. Do the benefits outweigh the costs?

The AI revolution is just starting – from generative AI took the world by storm, to the AI frenzy in the stock market.

The banking and financial services industry (BFSI) emerged as one of the leading AI-ready industries in NetApp’s recent Cloud Complexity Report, with 72% of respondents in BFSI having data infrastructure and management optimized for AI.

With the current macroeconomic and geopolitical uncertainty, how can BFSIs balance the need to manage costs and cybersecurity concerns while pursuing AI to maintain their competitive edge?

DigiconAsia sought out some answers from Steve Rackham, CTO for Financial Services, NetApp.

As the technology continues to develop at a considerable pace, BFSIs are seeking deeper understanding as to how they can adopt, where to utilize and when to deploy AI while balancing the challenges of increasing regulatory compliance for things such as ethical and explainable AI as well as data storage and security. 

BFSIs that wish to deploy AI at scale will see a much higher demand on storage capacity and possibly higher cost associated with cloud usage. Shifting to a more centralized, enterprise-level deployments for AI, organizations will increasingly demand for a silo-free, low-latency data pipeline in place across their data estate to process AI models at a faster rate, increase training and improve resource sharing.

With more data being shared and generated with AI-powered tools, the traditional approach to data security is no longer sufficient. By adopting a data-centric, zero-trust approach, BSFIs can effectively protect data and bounce back swiftly from disruptions. Having robust cyber resilience strategy in today’s data-driven era will allow BSFIs to stay compliant with regulations as well as build customer trust and confidence.

Seems like AI is no longer an option for BFSIs, but what are the key business benefits of AI adoption?

SR: BFSIs are facing more competition than ever – with the rise of smaller, more agile fintechs and born-in-the-cloud digital banks, customers have more choices. Traditional BFSIs have to adapt to this new wave of competition by leveraging the power of data and AI as a key differentiator in driving efficiency and innovation gains.

The advantage that these traditional players have over their newer competition is financial data on customers, but the sheer amount of this data can be overwhelming. By using AI to analyze and process such vast quantities of data, spot patterns, and suggest courses of action with increased reliability, financial institutions can not only speed up decision making but handle all levels of competition.

AI can also be tapped on to enhance customer experience. AI-powered chatbots and virtual assistants can provide 24/7 customer support, personalized financial advice, and faster loan processing, significantly improving customer satisfaction. GenAI can be deployed to offer personalized communications and marketing materials, catering to individual customer needs and preferences more efficiently.

Operational efficiency can be increased by having AI automate repetitive back-office tasks like document processing, data entry, and compliance checks, freeing up human employees for more strategic work.

In the area of risk management and fraud detection, AI can analyze vast amounts of financial data to identify fraudulent transactions, assess credit worthiness more accurately, and predict potential risks, allowing for better-informed decisions.

Lastly, in a highly regulated industry such as BFSI, regulatory compliance is absolutely critical. AI can streamline compliance processes by automating tasks such as Know Your Customer (KYC) and Anti-Money Laundering (AML) checks, ensuring strict adherence to regulations across multiple jurisdictions.

Amid continued macroeconomic and geopolitical uncertainty, how can BFSIs in APAC balance the need to manage costs and cybersecurity concerns while pursuing AI to maintain their competitive edge?

SR: Rising IT costs and ensuring data security are the two of the biggest challenges in the AI era, but they will not block AI progress.

Indeed, 49% of BFSIs noted a significant increase in data storage budgets in the last year, according to NetApp’s 2024 Cloud Complexity report. Globally, 61% of respondents noted the increased cybersecurity risk of managing the increasing complexity of data across hybrid cloud environments. To manage AI project costs, 31% of companies globally are reallocating funds from other business areas.

BFSIs can consider implementing cloud financial operations or FinOps, which refers to the practice of driving visibility and bringing financial accountability to the variable spending model of cloud. Essentially, finance and IT develop a series of best practice measures to quantify and provide insight into the associated cost of cloud usage.

The objective is to create a cloud financial model that balances commitment with agility, delivering the needs of the business, whilst continually monitoring and optimizing architectures to ensure performance and availability.

They should ideally deploy a solution that comes equipped with a holistic, integrated portfolio of FinOps solutions that supports an organization’s intelligent data infrastructure. In this way, organizations can unify cloud cost and infrastructure optimization, deliver analytics to maximize the efficiency and impact of every dollar spent in the cloud, and drive digital business growth.

Organizations should consider solutions that are able to integrate the latest AI/ML-powered cyberthreat detection with other cybersecurity tools under a unified, intelligent data infrastructure to enjoy the most comprehensive cyber-defense.

Deploying a unified control plane can also help organizations intelligently coordinate, update, monitor and ensure a comprehensive defense, including workload-centric, policy-driven ransomware defense at the storage layer to protect business-critical data. 

Looking ahead, in what areas do you see BFSIs innovating in data infrastructure and management?

SR: BFSIs hold a wealth of data from customers. Put together, these data points are highly valuable in helping BFSIs develop new, personalized service offerings. The power lies in the hands of BFSIs to unleash the full power of data and AI to gain an edge against competitors. By adopting an intelligent data infrastructure, organizations can protect, unify, and harness all their mission-critical data in real-time to deliver exceptional data-driven business innovation and outcomes.

At the same time, all these data come at a cost. By 2025, the global “datasphere” is estimated to reach a staggering 180 zettabytes. If the datasphere continues to grow at the current rate, we are likely to exceed a yottabyte (a million trillion megabytes) of data created in a single year by 2030.

Yet only 32% of data available to enterprises is put to work; the remaining 68% is not even used, according to IDC. The carbon footprint of storing all these underutilized data is equivalent to that of the entire airline industry.

Data minimalism is emerging as a systematic and effective strategy to tackle inefficient and wasteful data accumulation. By adopting data minimalism as the cornerstone of their data strategy, BFSIs can cut costs and make significant strides toward meeting their sustainability goals.

Lastly, given the critical importance of data privacy and strict compliance regulations in this industry, BFSIs will continue to invest heavily in cyber-resilience.

Generally, we do see greater awareness for an effective cyber-defense strategy, and organizations are taking a more serious approach to heightening their security posture in an ever-evolving landscape. The challenge for most organizations is moving from awareness into strategy, policy and action to create cyber-defense in-depth before they are the subject of an attack.