How Machine Learning is Changing Finance Forever

Innovation drives things forward no matter the industry that is on the receiving end. In finance, innovation over the last decade or so has seen the way that financial institutions, banks, and lenders interact with their customers and provide financial services change exponentially. Here at Wizzcash, we have seen first-hand how innovations to working processes have meant faster lending decisions and instant payments to our customers. This helps to ensure a seamless and convenient way to receive funds when you need them most in an emergency. Thanks to advancements in technology, the process of receiving any form of borrowing from short term loans, personal loans, mortgages to credit cards, has become much more streamlined for the lender to provide, while allowing them to remain vigilant with compliance checks, for the benefit of customers.

At the forefront of this is advanced machine learning that is in continuous development and looks set to continue to innovate the financial and banking sectors over the next few years. In 2019, the Bank of England (BoE) surveyed 106 firms that included banks, credit brokers, online lenders, and many other financial services about the development of machine learning and its use amongst them. It found that 75% of them already use machine learning in some form and a third of machine learning applications are used for a considerable share of activities in specific areas such as banking or insurance. So where is machine learning most prominent and how will it continue to change the face of finance forever?

The Machine Learning Difference

At a basic level, machine learning is a sub-category of artificial intelligence that can perform tasks that use little or no human intervention. Interms of finance, this means it has the ability to analyse large data sets, detect patterns and solve problems quickly. It can also help to generate analytical insights, support new products and services and in general help consumers benefit from more tailored, lower cost products. For lenders, this means they can become more responsive and effective in the products and services they provide.But what areas are seeing the biggest advantages?

Machine learning has helped to make fraud and money laundering detection a much simpler and effective process. Being able to reassure customers that the threat of fraud is being constantly monitored and detected quicker than ever before is very valuable in retaining customer trust. The way machine learning helps this is by providing real-time analysis of account activity, learning what is normal behaviour on a customer’s bank account for example and what is more suspicious such as a large withdrawal or payment, especially in a different country or from a different device. This use of advanced machine learning has changed how quickly fraud can be flagged without having to wait for a customer to report it or a bank employee to spot it manually.

In terms of customer service and client care, the rise of automated responses from live chatbot services or the implementation of Robo-advisors has revolutionised this area. With customers able to interact with an automated service that learns from any previous interactions they may have had with it, means someone can find the information they need on their finances much quicker and accurately than before – all whilst having a human-like interaction.

Credit risk management is another area that has benefitted from advanced machine learning processes. By gathering data and alerting clients of any trends and potential risks before they happen, it means companies using it can provide much more accurate market forecasts. Barclays this year announced a partnership with Simudyne to help evaluate new risk mitigation, with the bank able to develop computer models that simulate millions of future scenarios so that they can assess how they perform before implementing for real. In terms of consumer lending, assessing a new customer’s credit risk has become quicker and more accurate than ever. Equifax in February unveiled a new machine-learning credit scoring system, designed to utilise network modelling to assist clients when assessing risk, called NeuroDecision Technology. The idea is that it will help lenders approve more consumers for credit without taking on additional risk.

The Future for Automation

The advancements of technology have meant that without the need for human interaction, many job roles within the financial sector could disappear in the coming years. Nearly 72% of banks, insurance firms and other financial institutions are using artificial intelligence according to Microsoft last year, an increase in 7% in a year, and many of the processes involve automation which rose by 16%. By 2025, it’s predicted that machine learning, deep learning and cognitive analysis will replace 230,000 jobs within investment banking, according to management consultancy firm Opimas, with 90,000 jobs lost in asset management. Many time-consuming, administrative tasks are expected to benefit from the advancement of machine learning AI, but this means whilst productivity increases, many jobs could become automated. Research from Price Waterhouse Coopers (PwC) found that 30% of jobs in finance and insurance were at risk of automation by 2029, with 50% of all clerical roles facing automation.

As the advancements continue to change the way consumers and companies interact forever, it will be interesting to see how many of the automated predictions from machine learning processes become reality. Whilst there is a threat to jobs in the future, according to research by Headspring in July last year, 75% of people working in financial services do not use any form of artificial intelligence in their current role and 39% want companies to create a new job role to manage AI rather than replace. Like many lenders, at Wizzcash we will be keeping an eye out on the next big innovations for lending and how we can continue to help our customers benefit from the improvement it potentially brings.

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