Manoj Mistry discusses why significant investment in AI is important as banks must adapt to technological changes in Economy Standard

This article was originally published in Economy Standard and can be accessed here. 

Why significant investment in AI is important as banks must adapt to technological changes

Artificial Intelligence (AI) is set to turbo-charge financial services, transforming how they are delivered to customers. To some extent, this is already happening, as AI allows for more tailored products and services. For banks, it also creates process efficiencies, enhanced cybersecurity and increased risk mitigation. With appropriate safeguards, AI will create significant benefits for banks and customers alike. But as regulators focus on financial services, specific AI regulation seems inevitable.

So, what might regulators seek to do? Beyond setting standards to make AI-based services safe for consumers, governments are moving to draft laws that will affect financial institutions and supplement existing legislation. In striking the right balance, they must guarantee sufficient protection for AI systems users while ensuring that new regulations do not frustrate technological evolution and impede business investment in AI. Key drivers for new AI laws include:

• The limitations of existing data protection laws;
• The ethical implications of AI and protection of individual consumers and customers;
• Mistakes made by AI systems, e.g. facial recognition technology, autonomous cars or weapons; and
• The potential use of AI for criminal or other nefarious purposes.

Until quite recently, hedge funds were the primary finance sector users of AI and machine learning (ML). Now we see the spread of ML applications elsewhere, including banks, fintech companies, regulators, and insurance firms. Different use of AI and ML is having a significant impact on the financial sector – from accelerating the underwriting process, portfolio composition and optimisation, model validation, Robo-advising, and market impact analysis, to offering alternative credit reporting methods.

Deployment of machine algorithms to automate time-consuming processes is ubiquitous across financial services, offering customers a more personalised experience. Key areas where development of ML is commonplace include:

• Financial Monitoring – enhanced network security, training systems to detect flags such as money laundering, which can be prevented.
• Making Investment Predictions – enables fund managers to identify specific market changes more effectively than traditional models.
• Process Automation – allows financial institutions to replace manual processes by automating repetitive tasks.
• Secure Transactions – detect fraudulent activity by analysing multiple data points which can go unnoticed by human intervention. It also helps with the process of false rejections that improve the precision of real-time approvals.
• Risk Management – ML techniques can enable banks and financial institutions to significantly lower their risk levels through analysis of huge volumes of data sources. Unlike traditional methods, which are invariably limited to essential information such as credit scoring, ML can analyse significant volumes of personal information.
• Algorithmic Trading – an excellent example of effective ML usage, Algorithmic Trading (AT) has become dominant in global financial markets.

ML-based solutions and models allow trading companies to monitor trade results in real-time and detect patterns that move stock prices, enabling them to make better trading decisions. ML algorithms can also analyse multiple data sources simultaneously, giving traders a distinct advantage over the market. Other benefits include: increased accuracy and fewer mistakes; trade execution at the best possible price; significant human error reduction; and automatic checking of multiple market conditions.

Customer data management also improves. Diverse financial data from mobile communications, social media activity, transactional details and market data creates a huge processing challenge. Integrating ML techniques to manage large volumes of data can deliver process efficiency and allow the extraction of real intelligence. Data analytics, data mining and natural language processing help to obtain valuable insights which leads to greater profitability.

By using ML algorithms to analyse structured and unstructured data, decision making becomes easier because better information is available. Customer requests, social media interactions, internal business processes, and discovering trends can be used to assess risk and help customers make informed decisions.

Customer service also benefits. Using intelligent chatbots, customer queries are resolved: monthly expenses, loan eligibility, or affordable insurance plans, for example. Being able to make accurate predictions based on past behaviour makes AI and ML models a great marketing tool. Analysing mobile app usage, web activity, and ad campaign responses, ML algorithms feed into a robust marketing strategy.

As AI deployment increases, the future of finance will be heavily influenced by emerging fintech with technology setting the stage for ever increasing competitiveness between financial institutions. Global banking’s investment in AI will continue to grow. In the challenge to keep up, some smaller banks are concerned about the implications if they do not. Facing limitations on capital expenditure and budgets, further consolidation might follow.

IBOS expects greater activity in the financial sector as banks adapt to technological changes as competition is fuelled by AI and ML powered advancements. Banking plus AI make for an exciting future. Significant investment into these technologies will enable bigger banks to serve customers more efficiently, setting new performance standards and increasing revenue.