The stereotypical idea of a bank — elegant people sitting behind desks and stamping documents — is becoming history. Forecasts show that, by 2021, the majority of all financial operations in the UK will be done via an app. From the client’s perspective, banking has never been easier.
But simply migrating isn’t enough – technology marches ever onward and, now, financial companies need to embrace automation, as well as ‘just’ digitalisation. Amidst all of this, no solution has ever been as critical as the ongoing introduction of AI.
The Financial Sector & A History of Transformation
While AI might seem new, it’s actually just the latest in a long digital trend. The transformation of financial institutions kicked off around 10 years ago, when market leaders embraced new digital channels — and with them more effective product selling and a wider customer reach. In a sense, banks stopped being simply money keepers and givers; they became something completely different (something more!) — a type of technological service.
As the evolution continued to spread successfully, introducing modern technologies and software delivery methodologies like DevOps, Agile, Lean and Continuous Delivery, financial institutions have set their sights on other promising directions, such as legacy system modernisation and Cloud adoption.
Yet, as we already said, the most important of all of these is arguably the use of Artificial Intelligence (AI).
Furthermore, with the global digitalisation of the banking industry, new technologies are no longer seized as ‘just’ a chance of gaining the upper hand; they’ve become something of a standard that banks must comply with. This can prove challenging, especially to older institutions which now have to evolve into this new, modern form if they want to remain competitive. AI is just the latest hurdle but moving around it isn’t an option – financial organisations need to embrace the inevitable and charge through.
Luckily, All This is Achievable Through Modern AI
If financial institutions aspire to be at the cutting edge of technology and modern services, they need to adopt the latest trends in a fast and clever way. Currently, the market is pushing to get the most of AI — in particular Machine Learning — and implement it in various areas.
How you implement AI or ML depends entirely on your organisation and which factors are the most important, but we’re already seeing a number of vital emerging trends…
Credit Risk Assessment
AI, when deployed in risk management, brings two decisive qualities to the table: reliability and speed.
Machine Learning algorithms are very thorough. Compared to old, manual scoring systems, they can analyse more than risk analysts, who will usually only consider metrics such as income and statistics. AI technology is able to process insights generated from past transactions and utilise all sorts of data collected by banks in the past, predicting the actual risk of credit with a holistic approach. It can even take non-financial information into account — like current news from all over the world.
And all this faster than humanly possible. Literally.
Financial risk mitigation performed by AI systems trumps manual fraud detection in several areas. As mentioned above, Machine Learning algorithms analyse large volumes of data, granting previously unknown insights into behaviour patterns — which makes it easier to spot abnormal operations and instantly identify transactions that look out of the ordinary. As a result, statistics show that the detection of real fraud can be increased by roughly 50%.
Apart from the main role of detecting fraud, Machine Learning also helps in lowering the risk of damaging a financial institution’s reputation. After all, wrongly suspending an account for a fraud that didn’t occur usually kills customer satisfaction. Case studies have shown that current machine-based fraud detection systems can reduce false positives by nearly 80% – which goes a great deal towards avoiding such scenarios.
Digital Wealth Management
Artificial Intelligence can also serve as a well-informed financial adviser.
Similarly to credit risk rating, Machine Learning systems can analyse the behaviour of account holders and assess their spending patterns — in this case, to recommend optimal decision making. They can offer advice on how to maximise savings, propose tailored banking products and tools, or even point out potential financial mistakes (“Hey, Joan, did you notice you’ve spent £2,407 on Grande Latte last year?”).
The ultimate forms of digital wealth management are automated investing systems, which manage a portfolio of assets and single-handedly turn a profit. They already possess a positive track record, successfully managing hundreds of billions of dollars — and these numbers are projected to rise rapidly in the coming years.
Machine Learning can also gather diverse market insights and provide a sentiment analysis (interpretation and classification of emotions within text data), and a historical data overview, while also adding news gatherings into account.
Combined, all these sources can comprehensively indicate how large the risk of investment is and how to potentially maximise profits.
Tailored Offers — Personalised Marketing
All the information gathered and processed in the areas described above can also be put to use in sales. With a deep understanding of the customer, their needs and behavioural patterns, it is easier (and cheaper) to sell products and solutions that the client is more likely to be interested in buying.
Better user segmentation and marketing preference analysis is are not only benefit not only to financial institutions, but also to their clients. A customised experience, based on a detailed profiling, raises the satisfaction of the customer who is offered products they could most likely be interested in, avoiding opting out of marketing contact and a general dissatisfaction with the company’s sales tactics.
This list is far from over — and probably never will be! Among other interesting areas where AI can be implemented are also, for example, anti-money laundering, cyber-security or an automatic transparency and losses limitation analysis. But those are entire topics in themselves, perhaps for another day!
Many financial organisations already started integrating these new solutions into their systems. Implementing Machine Learning is just part of the transition — the other necessary ingredients include a unified approach to user experience on all levels of the business and, most of all, the ability to react fast to the changing environment, with experimentations and failures built into company culture.
As for now, one of the main challenges that especially bigger and older financial companies must face is, surprisingly, not the integration of modern systems itself but closing the gap between classical operations (banking from the “past”) and the “new world” of finance, as driven by changing customer needs (the previous idea of “having the money” is being replaced by the need of “using the money”).
Moreover, many financial institutions are still evolving ancient systems, some of which can even remember the ‘90s, while new user experience solutions must be based on modern systems. The technological standards in the financial sector are constantly rising due to the rapid growth of FinTech startups, not to mention that the finance world aims to eliminate paper money for more control and transparency. In other words – a completely digital financial sector is inevitable, soon or later.
Finally, a word of encouragement: for experienced companies with an established position on the market, a technological transition of this range may seem difficult, but as case studies show, the benefits are worth the trouble.