It comes as no surprise, I am sure, that artificial intelligence (AI) is no longer the novel plaything of backroom theorists. Everyone wants a taste of the proven value of AI — none more so than the region’s banking, financial services, and insurance (BFSI) sector. A Google-supported study from Economist Impact predicted that in the Middle East and North Africa, the BFSI industry will become the biggest investor in AI, accounting for a quarter of all spending. The report also indicated that AI in the banking sector alone could contribute as much as 13.6% to regional GDP by the end of the decade.
The appeal of AI to BFSI entities is obvious. Global banking has earned a reputation for turbulence in the past 15 years or so. Crashes, scandals, and unpredictable externalities like the COVID pandemic have forced the region’s bankers to contemplate how machine-learning models could optimize trading; or how pattern-matching algorithms could weed out fraud at a fraction of the cost; not to mention how the potential for customer profiling could allow them to individualize experiences.
But remember, AI technology is not a one-shot vaccine for what ails a brand or what drags down a balance sheet. It is quite simply a powerful toolkit. However, in the hands of the right “Everyday AI” culture, it can give an organization the edge it needs to succeed in its goals. These goals differ from industry to industry and from enterprise to enterprise, but for the regional BFSI industry, let us look at four use cases that will emerge in the coming months.
The involvement of major global BFSI players in the Net-Zero Alliance is a call to arms. The scene is set for significant movements on environment, social, and governance (ESG) issues across the industry, particularly in the GCC where per-capita emissions are some of the highest on the planet. As ESG cements itself in the fabric of KPIs, AI is an ideal tool for determining, for example, which environmental data is significant to an organization’s footprint.
AI platforms are also strong on governance (the G in ESG), enabling business analysts, data scientists, and engineers to collaborate on model development and deployment to ensure compliant and responsible AI without the need for expensive external consultants. One of the aspects of Everyday AI culture is that turning to the AI toolbox is the first instinct in solving a problem. Data democratization and cross-team collaboration — two of the hallmarks of Everyday AI — are prerequisites in effective ESG as well.
The more technology there is in an industry, the more efficient it becomes. But the same could be said of its exposure to crime. Financial malfeasance relies on technology, and new methods of money laundering are emerging that will require more than employees’ hard work to resolve. Regulators already look to BFSI firms as the frontline in the war against money laundering and they expect businesses to report accurately and often.
AI can step in to trawl through data at a rate unattainable by even the most seasoned and dedicated team of human experts. These anomaly-sifting solutions are already being used to great effect in detecting other fraud, such as that associated with credit cards, but anti-money laundering (AML) teams have been slower on the uptake. Expect this to change by year-end, especially in light of the plug-and-play solutions now on offer for the most common AML use cases. Machine-learning algorithms can integrate business rules into their findings to streamline analysis and diminish alert fatigue. This makes it easier for AML case managers to perform more effective triage while still retaining records of unpursued incidents for reporting purposes.
Lowering costs is as much a priority in BFSI circles as anywhere else. Managed appropriately, IT modernization and digital transformation can deliver smooth transitions into new ways of doing core things, without disrupting operations amid uncertainty. AI solutions can eliminate unnecessary, tedious grinds that plague teams of specialists. These experts then become equipped with tools for self-service analytics, AI governance, and cross-team collaboration.
In recent years, necessity has reinforced its role as the mother of invention. Firms did not choose to hybridize their workforces, but COVID lockdowns gave them no choice. Machine-learning tools can help with this ongoing transition, determining location-specific shift-staffing requirements, to cite just one example.
- Risk management
Who among us has not, at some point over the past few years, worried about the quality, coverage and future availability of our health insurance? Across the region, employees are demanding more from their entitlements and insurers must be prepared to satisfy expectations or risk becoming irrelevant in a market that is forever changed. AI can prevent an overinflating of the workforce while streamlining claims processing, individualizing policy advice, and detecting and preventing fraud. Providers will be able to bundle products and manage risk to remain solvent and relevant.
AI will also be better placed to digest and analyze new kinds of structured and unstructured data that allows insurance companies to better understand existing and potential customers. Managing data will also enable insurers to identify market opportunities early on without having to go through painful restructuring of their business models.
The edge is yours for the taking
AI adoption in the GCC BFSI market is not without its obstacles. Enterprises must take care to comply with privacy regulations and Islamic law. And they are beset by shortages of AI talent. But if they can adopt an Everyday AI mindset, talent can be grown from within while methodologies take shape that replicates compliance across datasets and projects. Competitive edge is there for the taking. All we have to do is grab it.