AI in Finance: 5 Use Cases That Will Revolutionise the Industry

Chris Schon
Apr 23, 2020


AI in Finance: 5 Use Cases That Will Revolutionise the Industry

What’s next in financial innovation?

Technology is driving huge disruption and innovation in the UK financial services industry. The advent of cheap cloud computing, combined with new open banking regulation and a young, creative generation of UK entrepreneurs means that nimble fintechs can now compete against the incumbents.

Take Monzo for example. A few years ago it would have been ludicrous to suggest that a new bank could snatch millions of customers from the hands of the high-street brands. Yet through their software, data and user-focused product, they have done just that. They now have over 3,000,000 customers.

As is typical with an industrial revolution, it is critical for existing (and new) businesses to drive their own technological innovations forward to maintain customers, improve internal processes and evolve as a business. Data and AI provide plenty of these opportunities, and here are some of the top use cases:

🤖 1. Process automation

Many aspects of financial services involve a series of tasks that can be boiled down to the manual validation, assessment and verification of data. This could be to broker a deal or accept an application for a product. Despite not being the most glamorous use case, the automation of such processes through software and AI-driven assessment will no doubt instantly increase worker productivity and reduce waiting times for customers.

🦹🏼‍♂️ 2. Fraud detection and anti-money laundering

In a world where money laundering accounts for an estimated 1–2% of global GDP, it is critical for every financial institution to ensure compliance and to do so effectively. AI helps enormously to do that. The ability for algorithms to efficiently process large amounts data from a variety of sources, identify problematic transactions and relationships, and report them in a visual tool will allow compliance teams to handle more cases than before, and better understand suspicious behaviour.


We have worked for a leading UK fintech company to build a large-scale, intelligent anti-money laundering system. Learn more about this
here.

📊 3. Better, fairer credit decisions (for SMEs too!)

Advanced data science and machine learning techniques can optimise and streamline previous rule-based or manual credit scoring systems; using sophisticated metrics and parameterisation to make better decisions. Fair, accessible credit is critical for a successful economy and any business offering it. With historical performances of business loans catching up with consumer records, scorecards for SMEs are on the horizon too.

💬 4. Chatbots

Customer service user journeys are often unsatisfying, and time is wasted by both consumers and agents identifying issues instead of solving them. You might think adding AI to the equation would only make this more tricky, but algorithms can be trained to make intelligent decisions and responses, accurately identifying issues and giving precise details of the problem for when an agent is needed to intervene. This means reduced human errors, streamlined processes and fewer communication problems. Most consumer banks don’t have chatbots in their apps or websites, and it’s no surprise that the challenger banks have integrated customer service bots as part of their offering.

📈 5. Alternative data for algorithmic trading

Alternative data in the financial services industry is any data source not traditionally included in algorithmic trading models. These models typically use pricing signals, company performance metrics and news sentiments to drive portfolio optimisations. Alternative data includes credit card transactions, geo-spatial and social media sentiment data and more. US hedge funds use this data to optimise stock portfolios and trading strategies through ahead of the market insights, but the space is relatively immature in Europe. However, it is maturing at a rapid rate in Europe and offers similar opportunities for intelligent, data-driven decision making within hedge funds and asset-management operations. Decisions can be automated or assisted through algorithmic definitions of alphas (signals that trigger a trade).


I’ve written an example case study about alternative data in the gaming industry, check it out
here.

The landscape of the financial services industry is shifting fast, with much of the change coming through technology. There are a vast number of opportunities for AI to play a key role in this, and those that don’t capitalise on these could be left behind.

“AI in Finance: 5 Use Cases That Will Revolutionise the Industry”
– Chris Schon twitter social icon Tweet


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