"AI is the 'new electricity' … just as electricity transformed many industries roughly one hundred years ago; AI will also now change every major industry."
That is a pretty powerful statement above by Dr Andrew Ng! However, we are all well aware that many industries have used Artificial Intelligence (AI) with varying degrees of success. Just as electricity has revolutionised industries in the past century, AI has the power to disrupt industries significantly in the next century. This article will showcase a few key examples of exactly how AI is revolutionising the finance industry.
A report by Citigroup, showed that after the technology sector, the financial services industry is the biggest spender on AI services and is experiencing exponential growth. This comes as no surprise, as AI has the potential to improve outcomes for both businesses and consumers. Besides that, the financial industry is a vast sector that includes banks, building societies, e-money institutions, mortgage companies, investment banking, credit unions, insurance and pension companies.
According to a joint survey by The Bank of England (BoE) and Financial Conduct Authority (FCA), two-thirds of respondents have live machine learning applications in use. These published figures are projected to rise at an exponential rate. Interestingly, the insurance sector has 100% live machine learning applications in use.
It is no doubt that AI is disrupting the financial industry, and we have compiled a list of how it is being applied in the financial sector.
Fraud Detection
Fraud is a financial crime that includes credit card fraud, false insurance claims, loan application fraud and organised crime, to name a few.
Fraud is an increasing concern for all financial institutions, from the largest global organisations to the smallest companies and partnerships. FICO recently reported that 4 out of 5 banks in their survey had experienced an increase in fraud activities. This figure is only expected to rise in the future. This is a concern for financial services as the annual cost of fraud is US$1 trillion across all industries, while the estimated annual cost of fraud in the UK stands at a staggering £190bn. Therefore, keeping on top of fraud is critical to a financial institution's existence.
To help fight fraud, specially designed AI algorithms that can detect anomalies or unusual patterns can be used effectively. Detecting subtle distinctions that often go unnoticed by humans is where AI algorithms thrive effectively and at scale.
Credit Decisions
Credit applications are on the rise, and the need for the approval process to be automated is more critical than ever.
Credit decisions have traditionally been made by humans using a scoring method that considers the borrower's previous performance. This can frequently result in bias, as well as a lengthy manual process.
So how can AI help? AI can help financial lenders to make more accurate and quicker decisions. Apart from automating and speeding up the entire process, AI can also accurately assess potential borrowers at lower costs and bias. This enables financial services to make informed decisions about their customers, helping lenders differentiate between high-risk and creditworthy individuals. Even regulated industries involving decision-making can now utilise AI, with advancements in the explainability of models as well as ethics in AI.
Risk Management
Risk management is a crucial area for financial institutions, as it handles a company's security, trustworthiness, and strategic decisions. The goal of risk management is to keep organisations safe from potential threats that might negatively impact a financial institution's functioning or profit. The threats themselves can be diverse and include, but are not limited to, credit risk, political risk, market risk, inflation risk, and legal risk.
AI is increasingly being utilised in financial services to develop models capable of analysing massive volumes of data to identify market trends, prioritise risks, and monitor risks accordingly. This enables financial services to manage risks effectively and efficiently.
Hyper-personalisation
Hyper-personalisation is personalisation taken to the next level. It uses AI to acquire relevant and applicable data to provide more personalised and customised products, services, and information depending on individual behaviour. As much as it is beneficial for the financial services to provide personalisation, there is also demand to be met as there is an increasing expectation from customers themselves to receive personalisation.
A report conducted by HSBC showed that hyper-personalisation would be the dominant trend for the next decade. This is because providing exceptional customer service is paramount to ensuring success in financial services.
Sophisticated AI-based algorithms are ideal in providing hyper-personalisation because of their ability to generate insights from clients' behaviour, social media interactions, historical transactions, their feedbacks and opinions.
Algorithmic Trading
Algorithmic stock trading also knows as "Automated Trading Systems", refers to the automated setup using complex mathematical formulas to buy and sell shares.
Buying and selling shares are traditionally done by humans who would analyse historical and current market trends. This would provide an understanding of how share prices would fluctuate at a time. The aim of the game is to buy shares at a low price and sell them when prices increase to make a profit.
AI is especially effective in this area, where vast amounts of data can be analysed to make swift and highly accurate data-driven trading decisions.
Hyperautomation
Hyperautomation (the term coined by Gartner), also known as Intelligent Process Automation (IDC) and Digital Process Automation (Forrester), refers to the use of advanced technologies like AI to automate manual processes.
Hyperautomation helps any organisation to not only help increase productivity but significantly lowers operational costs. Hyperautomation could be in the form of paperwork automation, monitoring transactions, use of chatbots and automating manual tasks in call centres.
The ultimate goal of hyperautomation is to automate repetitive and mundane tasks. This leads to freeing up time for employees to focus on other essential aspects of their job, increasing employee satisfaction. The additional benefit of hyperautomation is the minimisation of human error and ensuring accuracy every step of the way.
Gartner has recognised hyperautomation as one of the top ten technology trends for 2020, making it a crucial technology for organisations looking to automate processes.
Benefits of AI in the financial sector
AI is indeed the 'new electricity' and is continuing to revolutionise the financial industry.
To summarise the benefits described above, leveraging AI can make a tremendous positive impact whilst providing huge gains to a business.
Understanding how to incorporate AI ethically using explainable AI is critical in the finance industry. As experts, we understand precisely how to integrate explainability whilst developing specialised algorithms to meet business needs.
AI is disrupting many industries, and using AI is no longer a luxury; it is a necessity. We are extremely excited to work at the forefront of technology and help businesses through this journey.
Get in touch to understand more about how Advancing Analytics has helped several financial services customers deploy state-of-the-art machine learning applications and platforms. If you need support implementing any of the above examples within your business, or if you want to learn more about what data science can do for you, please contact us.
