4 Trends for AI Financial Services in 2023

Use Cases & Projects, Scaling AI John McCambridge

Unstable, uncertain, unpredictable: these are just three of the words that have become shorthands for the past three years in the global economy and world politics. Enterprises in the financial services and insurance (FSI) industry are very familiar with them — painfully so, in many cases. The global economic shock produced by the pandemic, followed by the disruptions to the supply chain caused by the war in Ukraine, have left many firms bracing for an uncertain near-term future and seeking to adapt their business to weather the worst of it. 

Many firms have turned to AI solutions, with varying degrees of investment, in order to streamline their processes, improve their forecasting abilities, meet ESG goals, head off the threat of fraud, and align themselves with increasingly stringent regulatory frameworks. Today AI delivers tangible and measurable business results, but as we’ve said before, it is no panacea. AI’s integration into a company’s culture and toolkit is a means of achieving practical and bounded successes that, cumulatively, give firms an edge over their competitors.

At FSI enterprises in 2023, some AI solutions will be prioritized over others. In this blog, we’ll take a look at four of the trends we expect to see driving the AI-adoption strategies in the FSI industry this year. 

Tech-Leveraged ESG

Though the past year has seen calls for Environment, Social, and Governance (ESG) philosophies and policies to be rethought in light of their frequent lack of nuance, most firms remain aligned on the overall value of ESG as a guiding framework and cultural ideal. 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 proof that the financial industry is gearing up to take on the challenge of sustainability via ESG frameworks. 

As ESG goals are being embraced as KPIs across the FSI industry, more companies are adopting AI solutions to help them hit their targets. The primary challenge they face is determining which signals — what environmental data, for example — are meaningful and which are not. ESG-oriented AI solutions are designed to help parse and overcome this challenge. When it comes to financial analysts, for example, AI can empower them to navigate a broad range of ESG signals and digest them into usable insights, as well as to wrangle and process unstructured data, as we discuss in this blog post

In the case of Governance, in particular, platforms like Dataiku enables line-of-business analysts, data scientists, and engineers to work collaboratively on model development and deployment within a system of controls that aligns with existing regulations and best practices. Tools like this allow companies to avoid having to hire multiple ESG experts to monitor various aspects of their governance operations; time, labor, and ultimately money are saved. 

The use of AI to help achieve ESG targets is a cultural shift as much as it is a technological one. Platforms that enable data democratization and seamless cross-team collaboration are making it easier to integrate ESG best practices into existing workflows, which, in turn, is melting away resistance to the very prospect of making ESG a priority in the first place. If, over the last few years, we’ve seen corporate leadership take up the mantle of ESG as corporate policy, in 2023 we’ll start to see this policy trickle down into industry culture. 

Expanding the Reporting Solutions Market 

As technological advancements in FSI firms increase, so too are financial crimes and new methods of money laundering. As regulatory policies grow more complex and far-reaching for the FSI industry, they are also targeting companies’ ability to prevent and root out various forms of financial crime in a reliable manner. FSI firms will need to devote resources and intelligent solutions to remain aligned with these fast-changing regulatory environments and, most importantly, to ensure they can combat nefarious and criminal activities directed at their customers and their infrastructure. 

AI solutions will increasingly be adopted to keep fraud and money laundering in check. These tools enable data teams to sift through massive quantities of data and sort the signals from the noise when it comes, for example, to detecting point anomalies, contextual (or conditional) anomalies, and collective anomalies. Whereas teams working on fraud detection at companies across the industry have already adopted AI-driven approaches to detect and thwart credit card and other forms of fraud, those working on anti-money laundering (AML) have, until now, been slower to integrate AI solutions into their work. This is beginning to change, however, and in 2023 we’ll see more AML work involving AI from a regulatory perspective and within teams.

Dataiku offers plug-and-play solutions for some of the most common use cases in fraud, financial crimes, and money laundering. Our Credit Card Fraud solution provides a unified space for fraud-fighting teams to manage business rules alongside machine learning (ML) approaches. And our Anti Money Laundering (AML) Alert Triage solution makes it easy for AML case managers to prioritize investigations that tackle likely true positives first and without challenging additional regulatory burdens — as no alerts are discarded; to easily integrate this solution into their existing AML processes; and to provide insights which can be used to review the effectiveness of some business rules as a distinct project.

AI for Greater Operational Efficiency

It’s hard to know which way the economy is going. But whether or not we are currently in the grips of a recession, most industries are facing the prospect of a downturn more broadly with a belt-tightening attitude. In 2023, the name of the game for many will be cost reduction by reliable means. 

IT modernization will be one of those means, as it represents a kind of transformation that won’t upend everything, which is the last thing firms want to do in the face of uncertainty. AI solutions to problems that, in some cases, used to be tackled by large teams of specialists will pave the way for leaner, more powerful teams enabled by self-service analytics, AI Governance, and efficient cross-team collaboration. 

Companies that have been on the fence about investing in AI, waiting for the right moment to take the leap (actually, it’s not so much a leap as a step onto a new path), will find extenuating circumstances making the decision for them. Changes involving workforce hybridization (reducing physical footprints and shifting employees to remote work) and reductions in the workforce will be helped along by machine learning tools that, for instance, can be used to help determine location-specific shift staffing requirements. 

Personal Risk Awareness

Concerns about the quality, coverage and future availability of government provided health and retirement coverage has produced a growing global demand for the kinds of insurance that used to be provided solely by government-run programs — medical care and retirement programs, in particular. In 2023, insurers need to be prepared to meet this demand without overflowing their systems or over-inflating their workforce. They need to meet it intelligently, in other words. 

The good news is that, over the past few years, many firms in the industry have been adopting AI solutions that help them streamline claims processing, give out tailored insurance advice, detect and prevent fraud, bundle their product offerings, and manage risk. All for the best — studies have shown that insurers who invest in AI are increasing net new business by 20%-25% and reducing loss ratios by 2 to 3 percentage points. 

As the demand for certain kinds of insurance increases over the coming months and years, so too will the volume of structured and unstructured data about potential and would-be customers inflate insurers’ databases. Investing in AI tools, like Dataiku, for managing that data will empower insurers to capture that demand in the near-term, without needing to make massive structural changes to their businesses.

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