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Now that financial services firms have made it through the early phases of digital transformation – moving services online, modernizing front-office applications, and embracing mobile finance – the next wave of modernization is already picking up a head of steam. 

According to a study by research firm IDC, global spending on AI systems will jump from $85.3 billion in 2021 to more than $204 billion in 2025. Financial services (along with retail) leads the way in aggressive AI adoption. In fact, a study by the Economist found that 86% of financial services executives intend to increase their AI-related investments through 2025. 

However, while AI creates efficiencies and opens up new revenue opportunities, AI also challenges organizations in ways that previous technologies did not.

A recent survey by Accenture of 500 licensed financial advisors in the U.S. and Canada found that 50% are struggling to execute their AI vision, and 55% find AI technologies too complicated to be practical. However, the same survey found that even with speed bumps, 92% of financial advisors say their firms have already started to adopt AI. 

With AI coming whether your organization is ready or not, here are four ways financial services firms can smooth the transition:

1. Develop a big-picture strategy for AI adoption throughout your organization

One AI adoption trap that many organizations fall into is piecemeal AI adoption, with each department doing its own thing without coordinating with the rest of the organization. Deloitte recently studied what separates AI frontrunners in financial services from the rest of the pack and found that the number-one trait frontrunners shared was embedding AI into enterprise-wide strategic planning.  

2. Involve customers early in the process

As you pilot AI-powered applications, be sure to involve customers early. Deloitte found that using AI to enhance customer experiences is the second-most common trait shared by AI frontrunners, and according to the Economist’s research, customer satisfaction is the number-one way to measure the success of AI initiatives.

It’s important to involve customers and all stakeholders in the adoption process, so that their feedback, rather than best guesses, guides the process.  

3. Center experts 

When unsupervised, AI algorithms are not terribly accurate. The typical failure rate for unsupervised AI is around twenty percent. Supervised AI, in which humans help algorithms learn, is better, but to get the most out of their AI investments, organizations should find ways to layer AI around experts.

Rather than attempting to replicate experts on the cheap, AI that centers experts, or Expert-in-the-Loop (EITL) AI not only has the advantage of greater accuracy, but EITL AI is able to work with experts to tackle much bigger problems, such as discovering new polymers or improving aircraft safety.   

4. Mitigate risks

An overlooked way AI can deliver high ROI is through risk mitigation. Cybersecurity and fraud detection are two obvious ways AI can reduce risks, but there’s another area that can deliver outsized ROI through risk mitigation: regulatory compliance. 

Financial services firms are among the most heavily regulated businesses in the economy. Keeping up with regulatory change requires a department of well-paid experts. Unfortunately, those experts spend much of their time on manual, repetitive tasks. For instance, a Thomson Reuters survey found that the typical compliance officer spends more than thirty percent of each week manually tracking and reacting to Enforcement Actions

AI-powered regulatory tools, or RegTech, rely on the same core technologies as modern FinTech tools (AI, Machine Learning, Big Data analytics) to deliver quick insights to experts who can now focus on higher-value strategic activities that benefit the bottom line. 

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