AI is already changing the way financial firms operate, and that change is only going to accelerate. What single change would financial services executives wish for from one wave of a magic wand? Broadridge posed that question to the C-suite executives participating in its latest Digital Transformation and Next-Gen Tech Study. The result was a clear: more artificial intelligence. AI is already changing the way financial services companies operate, and that transformation is poised to accelerate as firms target further expansion and integration of AI as a top strategic priority.

The Bank of England has published its report "Machine Learning in UK Financial Services". The report sets out its findings, following a survey of around a hundred regulated firms in the UK. It highlights the growing use of machine learning, especially in insurance, and the challenges of explainability, legacy systems, the skills gap and regulatory uncertainty. The number of UK financial services firms using or developing machine learning (ML) applications is increasing, and this trend is set to continue across a greater range of business areas within financial services. The largest expected increase in use, in absolute terms, is in the insurance sector, followed by banking.

They can also be used for financial fraud. Fraudsters can use deepfake technology to trick employees at financial institutions into changing account numbers and initiating money transfer requests for substantial amounts, says Satish Lalchand, principal at Deloitte Transaction and Business Analytics. He notes that these transactions are often difficult, if not impossible, to reverse. Cybercriminals are constantly adopting new techniques to evade know-your-customer verification processes and fraud detection controls. In response, many businesses are exploring ways machine learning (ML) can detect fraudulent transactions involving synthetic media, synthetic identity fraud, or other suspicious behaviors.

Artificial intelligence (AI) is often touted as the cure-all for financial services firms' ability to deal with the looming data onslaught stemming from environmental, social & governance (ESG) regulation. Yet ESG also poses an existential threat to the financial services industry's use of AI The European Union's Sustainable Finance Disclosure Regulation has required asset management firms to begin collecting millions of data points from the companies in which they invest, and the forthcoming Corporate Sustainable Reporting Directive will only add to the volume of data points. Further, there is the data being collected under the Task Force on Climate-Related Financial Disclosures (TCFD) initiative and the International Sustainability Standards Board's plans to create a baseline for ESG reporting. Taken all together and it becomes clear that AI-enabled systems will be essential to firms' efforts to make sense of -- and profit from -- all these requirements. The carbon footprint from storing and processing data is enormous and growing, algorithms have already been shown to discriminate against certain groups in the population, and a lack of technology skills in both senior management ranks and the general workforce leave firms vulnerable to mistakes.

The overarching concept of digital transformation is to use technology to replace manual processes with automated, digital ones and to replace older, legacy systems with modern, agile technologies. What was historically seen as "a good idea for the future" or a nice add-on or update to an outdated system has now become mission critical. Digital transformation has become the benchmark for survival in the financial market, making it even more critical for success and innovation. As next-gen technology continues to transform, financial services companies and their executive teams are bracing for the next phase of digital transformation. And most recognise the need to embrace this acceleration towards digital but are still too early in their journey to undergo technology innovation, tech productivity and platform modernisation, which will not only set them up for success in the marketplace, but also set them apart from competitors.

People have become very familiar with the term artificial intelligence (AI), but many of its users have only a rudimentary understanding of how it actually works. As a result, to date financial services and many other industries have yet to leverage its full capabilities. For financial services firms, adoption of explainable AI could drive adoption of AI-related technologies from the current rate of 30% to as high as 50% in the next 18 months, according to Gartner analyst and vice president Moutusi Sau, adding that lack of explainability is inhibiting financial services providers from adopting/rolling out pilots and projects in lending and from offering more products to the "underbanked" -- those who don't seek banking products or services, many because they don't think they will qualify. Moving to "explainable AI" will remove much of the mystery around AI, and, as a result will drive adoption of more AI-driven services experts agree. The Global Explainable AI (XAI) market size is estimated to grow from $3.50 billion in 2020 to $21.03 billion by 2030, according to ResearchandMarkets.

When these events occur, decision-makers within financial organisations need to analyse every scenario and act quickly – using data-extracted insights to guide the way. In this piece, InterSystems' Director of Product Management Jeff Fried explains how machine learning can play an integral role in building resilience within the financial sector, using real-time examples such as automated trading, fraud detection, and customer experience (CX) initiatives that utilise modern data management and analytics capabilities to make it through the most strenuous business challenges with almost no effect on the operations of the organisation itself. Before the pandemic, increasing business agility was of utmost importance for organisations across industries. But what was once a battle of speed and first-to-market has now expanded its focus to include business resilience. Recent events have underscored the need to weather the storm during volatile operational or security-related situations.

New technologies have enabled banks, insurers and other financial services firms to overhaul their operations and identify different ways of serving their clients. Over recent decades, innovative products have transformed the financial services industry – from payment types including credit and debit cards, to transaction processing such as telephone and online banking, to saving options such as investment funds and structured products, to e-commerce for financial assets, to risk management techniques, and beyond. Financial services firms must embrace the opportunities offered by innovation and further integrate disruptive technologies, such as artificial intelligence (AI), advanced analytics, robotics, the cloud and blockchain, to enable new services and capabilities. While there is still a place for traditional banking and financial services, customer expectations and preferences are evolving. According to VMware, almost half of UK consumers prefer to engage with banks via apps rather than in person, while two-fifths believe their smartphone is more important than their wallet in powering financial transactions.

"The global AI agenda: Promise, reality, and a future of data sharing" is an MIT Technology Review Insights report produced in partnership with Genesys and Philips. It was developed through a global survey conducted in January and February 2020 of over 1,000 executives across 11 different sectors and a series of interviews with experts having specific responsibility for or knowledge of AI. The article below is an extract of the full report. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review's editorial staff.

Like most industries, the financial services sector is being rapidly redefined by emerging new technologies. No longer limited to operating at the transactional level (think mobile banking and digital payments), increasingly advanced tech is encroaching into the more human-dominated roles within the sector. A Bank of England survey recently reported that financial services firms expect to see significant growth in their use of technologies such as Machine Learning over the next three years. These technologies are expected to be brought in to automate processes such as decision making – relying on algorithms to reach conclusions in a quicker, more accurate manner than a human. Aside of these obvious benefits which serve to boost company and industry performance and profitability, there is the added expected advantage that, by gathering the data necessary for this process to occur, the company can also discover new insights that can be exploited for commercial gain.