When thinking about the future of AI in financial services, it’s exciting to consider the endless possibilities. In the abstract, terms like “revolution,” “transformation,” and “sea change” captivate our attention and our imaginations. Will the future of FSI look completely altered? Or will it look much the same as now, with a few small but significant differences?
While it’s important to dream of the coming horizons in grand terms like this, it’s equally necessary to take stock of how AI and advanced analytics are actually changing the finance industry today, here and now. In other words, let’s get specific: how are these technologies being used now? What use case can they be mobilized to help with and solve?
In the spirit of answering these questions, we’ve compiled a living library of use cases for AI and advanced analytics in financial services. In this blog, we’ll run through the broad strokes of what you can expect to find in and gain from it.
The AI Landscape in Financial Services
While data is at the core of financial services, the data-driven augmentation and optimization of all banking and investment processes is far from being a market-wide reality. Trendsetters have been combining data empowerment programs for their business professionals with a transformation of core processes through advanced analytics to reap full benefits of the AI opportunity.
Thanks to comprehensive data strategies, financial services players can enhance their entire value chain and develop competitive differentiators across all critical dimensions:
- Nurturing and growing their customer base
- Enhancing risk management and regulatory compliance practices
- Streamlining all operational processes for improved resilience and efficiency
- Accelerating ESG embedding across the banking value chain
- Empowering financial professionals with data-driven decision making accelerators
- & much more
Here at Dataiku, we work across dozens of use cases in financial services, from highly specific tasks to general data process improvements. Many data teams, analysts, and modelers still build processes and models on cumbersome and overcomplicated spreadsheets. But in using spreadsheets and other legacy systems for business-critical analytics, teams stifle their growth and create compliance remediation debt that will eventually need to be paid.
By moving away from these, teams increase the quality and speed of outputs as well as the complexity of their data analyses. They can escape the risk of their spreadsheet freezing, crashing, or losing information when a dataset is too large, and move on to processing large amounts of data, incorporating unstructured data processing, working with predictive models, accelerating model risk reviews, and more, all within a well-governed and auditable environment.
Fully integrated and centralized data practices that support an upskilled and data-savvy workforce across all of an organization’s departments are the future of data science for banks. And as large language models (LLMs) become more sophisticated and more deeply embedded in AI models and processes, this future will arrive at an increasingly accelerated pace.
Platforms like Dataiku remove the risks associated with heavy use of spreadsheets and fragmented set-ups across myriad departments, providing instead a central, governed hub from which teams can work on and track project goals, inputs and outputs, and inner workings (like data sources), as well as track planned future project enhancements.
Common Use Cases in FSI
From risk management and operational efficiency to ESG, there are dozens of ways banks can harness AI, data, and analytics to drive value at scale — all while minimizing costs and saving time. Here’s a brief look at some of the common use case categories for which a robust AI and advanced analytics strategy can be a game changer.
Nurture & Grow Your Customer Base
AI-powered solutions for sales and marketing teams help both departments create efficient, data-supported systems that yield real results all the way through the customer journey. For example, AI action systems provide recommendations for sales teams that help them manage the sales process and ultimately make the most desirable offers for clients.
As marketing and sales are interrelated, marketing teams will similarly benefit from AI-based messaging recommendations. From marketing basket analysis to "also bought" recommendation engines, there is a lot of opportunity for banks to become more efficient and effective with data.
Dataiku works across customer-facing use cases to help banks and insurance providers place their good and services in front of the right customers at the right time. Here are some of the top, table-stakes use cases banks should be exploring today.
Enhancing Risk Management & Regulatory Compliance
As the everyday use of AI grows across many industries, organizations are experiencing a shift in the culture around data-driven decision making. However, the use of AI, like any other technology, comes with a certain amount of risk. Effectively implemented oversight, management, and clear, value-driven organizational priorities are therefore crucial for safe scaling.
The concept of model risk management and a culture of governance are well established in the financial services sector, where fraud, anti-money laundering, and cyber security teams (among others) are increasingly making intelligent use of advanced analytics to keep the ship of the enterprise afloat and headed in the right direction.
Improving Process Resiliency and Efficiency
Improving process efficiency seems like a no-brainer, right? To optimize a process, simply find the weak points in its flow and adjust any and all relevant steps of the process to reduce bottlenecks and the risk of disruptions. But simple as this sounds in theory, the complexity of today’s processes makes this optimization a very challenging endeavor. Thankfully, digitalization immensely helps us make sense of the large number of processes in organizations and the many ways they could be more optimized.
Accelerate ESG Embedding Across Processes
The race to ESG continues to accelerate in the financial services space. All major players have now taken firm commitments to embed Environmental, Social, and Governance (ESG) criteria in all their critical processes, with a strong focus on global warming management. The creation of the Net-Zero Alliance by 43 major banks including Société Générale, Citi, and Morgan Stanley and main insurance players including Aviva and Zurich insurance is yet another sign that the financial industry is getting organized to play its role in taking up the climate change challenge. The impact financial players can have is also more and more visible, as shown by the recent shift in Exxon’s positioning on climate change emissions in response to shareholder pressure
But commitments are no easy things to take. All financial organizations are well aware that words have an impact and that scrutiny on alignment between commitments and actions is a must-do. Now that decisions are taken, the next step requires the firm acceleration of business processes revisiting — where financial analysts play an essential role.
Accelerate Data-Driven Decision Making Processes
The key to making informed, data-driven decisions is a robust means of gathering, cleaning, triaging, and using the data one has access to. The challenge is increased when that data is pulled from sources external to the enterprise, such as news and other media, or image databases. In such cases, analysts are also rightly concerned about security and privacy: the data in question might well be sensitive, and so implementing a process that minimizes risk and maximizes security is a must. The right platforms and approaches will help data teams and analysts sift the signal from the noise in a responsible way.
Augment Business Function Processes
AI and advanced analytics hold immense potential for finance firms in augmenting their basic business functions. By leveraging these technologies, institutions can enhance deposit activity forecasting, optimize FX P&L modeling, improve IT efficiencies, and streamline human resources tasks — among many other use cases. The integration of AI across business functions not only boosts operational performance but also enables analysts to make data-driven decisions — and in many cases to benefit from self-service analytics — leading to increased competitiveness and long-term success in the dynamic financial landscape.
Discover More Use Cases
The list doesn’t end there. There are dozens more use cases to explore and discover, including those specific to the major domains of banking — from asset management to investment and commercial banking. Whatever your role, there’s almost certainly room to improve your processes with AI and advanced analytics.