As an IT leader in the financial services (“finserv”) sector, I often reflect on the challenges we face in today’s rapidly evolving landscape, as well as the best ways to overcome those challenges. I’ve seen my share of changes in this industry and have always recognized that finserv is a balance between art and science. Now with tools and technologies such as AI, predictive analytics and more, I appreciate that there’s more emphasis on the “science” part, and trending more so, to gain operational efficiencies, reduce costs and improve profitability.
In my role at Rimini Street, I help financial institutions navigate business complexities such as funding innovation, operational stability, compliance and more, all through the power of robust enterprise software support and innovation solutions. These days, AI is top of mind for finserv leaders, and rightfully so. Banks have an unbelievable amount of data that has just been sitting underutilized; but now with AI, there are an endless number of ways to transform that data into something much more usable.
This takes properly applying AI – at the right place, at the right time, with the right strategy behind it.
The growing demand for AI-driven solutions in ERP
Recent insights from a Censuswide survey we conducted with nearly 3,000 CIOs and CFOs revealed that 85% of responding CIOs in financial services globally are either investing in or planning to adopt AI technologies in 2024. Our survey also revealed that 71% of CFOs are increasing their IT budgets, reflecting a strong commitment to emerging technologies like AI-driven solutions.
This is a trend that will continue to grow, and the success of it hinges on the collaboration between the CIO and CFO to set and share the same vision. Both the selection of the AI tools as well as funding the investment will require strong communication and agreements from both parties.
Improving profitability with AI in financial services
AI can serve a powerful role in financial services, with continuous real-time monitoring of data streams that can help institutions maintain reliable and accurate data and improve their decision-making. For example, with AI, risk profiling of a new loan customer can take minutes versus days, quickly modeling what kind of consumer they will likely be, and the probability of this loan being paid on time and in full. With this level of information, finserv can reduce risk while the customer can get faster responses from the institution to take next steps.
Another interesting way AI helps with profitability is fraud detection. JP Morgan uses AI to analyze large volumes of transactions, customer and device data to effectively detect anomalies and potential fraud, finding patterns not easily seen by humans. By crawling data across multiple databases, banking organizations can reduce risk and increase profitability while requiring less headcount to do the work of many. This is particularly welcome as rising costs and availability of talent resourcing continue to be a concern in the market.
However, it’s important to remember that the effectiveness of AI relies heavily on the quality of the historical data that you “feed” it. In our recent survey, 60% of CIOs in financial services acknowledged that their historical ERP data requires substantial cleanup to fully leverage AI. The adage, “garbage in, garbage out” is particularly poignant, and focusing on data cleanliness and quality of data first will help to ensure reliable outcomes that can support business goals.
AI in financial services and banking: Proceed with caution
While AI in financial services can unlock new capabilities, we must be sure that we’re adding those capabilities into a model that’s flexible for the future and that can grow and expand as new features come into the marketplace. That’s why I work with CIOs and CFOs across the world, to help them:
- Fund innovation by saving on the costs of maintaining databases, ERP and applications
- Embrace a composable ERP strategy that allows you to add AI and other capabilities to existing systems without upgrades, migration or reimplementation
- Accelerate innovation by offering an improved UX layer that lives above the intricate and complex systems of applications and databases through our partnership with ServiceNow, Inc.
I share with leaders why it’s important to preserve customizations unique to their business, as these provide valuable data that drives differentiation from competitors. I also highlight why locking yourself into a new product upgrade just for a few new features can further delay the ability to take advantage of AI innovations. Instead, by maximizing the value of existing systems and enhancing them with best-of-breed AI capabilities, institutions can achieve the desired ROI and results – and do so faster.
The future of financial services is bright with AI
Looking forward, I see tremendous potential in AI’s capability to deliver a richer customer experience, whether that’s for corporate clients or consumers. The data that has just been accumulated for decades can now finally be activated and used to better understand how to deliver a curated banking experience that’s fast, accurate and secure.
One of the areas that I’m most excited about is how greater financial inclusion can lead to poverty alleviation. As finserv organizations gain more efficiencies, they can empower consumers to access banking services at a lower cost. The removal of value chains and of unnecessary hurdles allows for the concentration of revenue for small business owners, such as a coffee bean farmer who can now sell direct, bypassing intermediaries. This is a future that I’m proud to help make possible, and I invite you all to join me on this journey. See how we help IT leaders in the banking and financial services sectors focus more time and resources to driving innovation.
