Most financial institutions today were built over decades, with legacy systems developed using proprietary software designed for a completely different era. These legacy infrastructures struggle to cope not only with the demands of today’s modern financial system, but also the immense amount of data collected from customers.
When Covid-19 first hit, the initial few months laid bare the flaws and shortcomings of these financial institutions’ existing operational and technical processes.
In fact, Publicis Sapient’s Voice of Customer Study on consumers’ COVID-19 response towards financial services showed that over 1 in 5 Singaporeans did not lean favourably when asked if their primary bank’s online and mobile banking services were meeting their general financial needs.
Nevertheless, while the pandemic didn’t precipitate a collapse of global banking infrastructure, it did light a fire under the collective banking institution to fast-track transformation initiatives that were meant to be rolled out over the medium term.
Reflecting this, DBS’ Digital Readiness Survey revealed that 70% of large corporates and middle-market companies in Asia Pacific (APAC) have a digital transformation strategy in place, and that the pandemic has increased the urgency to act.
In fact, 97% of respondents indicated that they are facing external pressure to transform digitally as the pandemic accelerated the demand for contact-free services and questions the resiliency in the supply chain.
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Unfortunately, a sizable 53% of large corporates and middle-market companies in the region remain in the nascent stages of digitalisation, having just started developing their digital roadmaps or with current plans remaining underdeveloped.
To this effect, financial institutions are beginning to reimagine their role in the world, propelled by new developments in technology, new ways of thinking, and new growth models designed with resilience in mind.
Engineering transformation and cloud enablement is replacing old technologies and processes, while also putting them in a better position to handle disruption from digital newcomers in the fintech space, by accelerating the development or the application of available technologies that can positively impact their own capabilities.
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To be sure, banks are cognizant of the potential long-term benefits that technologies like Artificial Intelligence (AI) and Machine Learning (ML) bring to the table, which can come in the form of better customer experiences, or even to simplify risk management and improve fraud detection.
Let us break down a few ways that AI is having profound effects on financial services.
Customer onboarding made simpler
Customer onboarding, or similar Know Your Customer (KYC) processes, for any regulated banking product and offering, is one that requires numerous documents as well as corresponding checks to verify a person’s identity.
AI can speed up this process by using facial recognition and optical character recognition (OCR) processing to scan and pre-populate forms with the right data from official documents.
Managing customer risk profiles
Cloud services and infrastructure have made it easier to process massive data sets, as well as test and validate new customer credit risk models by using AI and ML to help appraise customer credit histories such as financial transactions and other related data. This delivers more accurate credit scores for individuals as well as entire portfolios, helping banks to better anticipate issues with loans or potential fraud, or even to cut down on loan approval times from weeks to mere minutes.
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This can be a partial solution for banks to address potentially underserved millennial customers who may not have sufficient credit history, or simply because large financial institutions simply don’t bother to make their services accessible either by setting high account minimums, charging exorbitant fees, or having onerous customer onboarding processes. More so because banks’ usual methods of measuring consumer risk using demographics and behavioural aspects like occupation, residence, and income may no longer be as effective when it comes to younger consumer groups.
Fighting fraud
The ongoing pandemic has not only seen a massive increase in incidents of banking fraud, but also fraudsters that are employing increasingly sophisticated methods as well.
Platforms like BioCatch tap on AI to retrieve behavioural insights that can deliver advanced fraud protection; these can be used by banks to support Anti-Money Laundering (AML) efforts, which are traditionally costly to implement and execute due to an increasingly complicated regulatory landscape, and more and more sophisticated methods that fraudsters employ to hide their tracks.
Traditional rules-based monitoring only allows for the detection of known scenarios. AI can help augment this by using risk-based and behavioural assessments in monitoring customer activity to spot irregular behaviour.
At the same time, these AI-based platforms can also potentially reduce the number of false positives flagged, which in turn means saving on unnecessary compliance costs. Mastercard, for example, logged a three-time reduction in fraud, and a six-time reduction in false positives using AI.
Looking ahead
The market opportunity that is brought to the table by tapping on AI and ML is by no means small; the market opportunity for financial services spending on AI in APAC is estimated to reach some US$4.29 billion by 2024.
Still, it must also be said that the examples above are not the only ways to apply AI to the finance industry. The applications available today are vast in number, and new solutions are continuously being developed.
However, it is more important for financial professionals to distinguish hype from reality, and to understand where and how AI and ML can be best applied to support current challenges.
It is also equally important to pace with how these technologies are evolving to address the challenges of tomorrow, whether tackling increasingly sophisticated fraud, making banking available to even more people, or even in reaction to the next unforeseen event.
Andrew Male is a client partner at Publicis Sapient
Photo: Mild Fakurian/Unsplash
