Digitally driven, Data-centric Finance

Digitally driven, Data-centric Finance

How Weak and Strong AI are facilitating Digital Transformation in the Financial Services Industry

Financial institutions are making a rather drastic change. For years, the finance industry has dabbled with Fintech and other digital technological innovations – experimenting with several ideas and here and there, but never incorporating any kind of radical change. More often than not, these innovations fit into existing structures without really impacting strategic decisions or bottom lines.

But now, with the COVID-19 pandemic changing the way we work, financial institutions are entering long term partnerships with technology firms and digitising internal as well as external processes, completely altering their degree of dependence on technology. Even firms at the early stages of adoption of certain technologies prior to the pandemic have had to accelerate their adoption speeds given the current needs. Let us, at no point forget, that the coronavirus pandemic has forced upon financial institutions several decades worth of transformation to be condensed into a few months.

It should, therefore, also come as no surprise that digital transformation processes have already started paying dividends for businesses. In the world of Finance, it is AI that is playing a defining role – quickly becoming the cornerstone for the industry in the coming decade. Almost all aspects of the business – sales, marketing, operations, research, HR, customer service – are rapidly undergoing business model changes.

The greatest challenge has been the need to reduce manual intervention in operations and customer interaction and hence, improve safety and soundness across the enterprise. This is the primary factor driving the adoption of AI and machine learning-driven solutions. The areas of primary focus right now are stability and resilience. Hence, akin to major big tech firms, financial institutions too are primarily becoming digitally driven data-centric enterprises.

Carrying out the digital transformation process is an arduous task – whether it be firms digitising individual aspects of business using Narrow (or Weak) AI, or carrying out major, more firm-centric, overhauls to existing systems using Strong AI.

Weak AI

Weak AI algorithms are set to complete specific tasks, and are customarily bound by a specific algorithm, not going beyond it in any way. It is still a smart cognitive engine designed to carry tasks out intelligently, but usually operates in a pre-programmed framework – such as Amazon’s purchase suggestions, Facebook’s news feed or Apple’s Siri. Although Siri appears intelligent, it essentially works within a seeded framework and can’t really work ‘intelligently’ beyond it.

Weak AI is still rather useful in simulating human intelligence and automating simple time-consuming tasks and (sometimes) even analysing data beyond human capabilities. It does so by analysing big data and drawing meaningful insights from it by detecting patterns and running simple predictions.

Client Risk Assessment is absolutely crucial to the financial services industry. Regular serviceable data such as client information, regional demographics, their basic financial data (existing investments, mortgages, credit cards, transactions etc.), their spending and loan repayment habits are certain aspects being serviced and automated by Weak AI. It saves on time spent in analysing said information and in generating insights about individual clients or a particular cohort of similar clients.

Personal Financial Management (PFM) is another field where AI is making great advances. There are currently a plethora of budgeting or wallet-based apps in the market, meant for consumers to make smarter decisions about their money and the way it is being spent. The algorithms being built accumulates all data from a user’s web footprint to create spending graphs, saving the time and effort involved in creating lengthy reports or spreadsheets to track one’s finances. Although privacy concerns still loom large, an AI watchdog for all your investments seems to be the way forward.

Strong AI

Strong AI carries with much larger prospects and potential than its counterpart. Broadly classified as general intelligence, it functions much like the human brain, with beliefs, cognition and perception programmed into it. However, sceptics of strong AI do argue about the degree of business success it can bring, or the level of intelligence it possesses, and hence may choose Weak AI – capable of optimising specific predefined tasks – over it.

The Financial services industry, however, has greatly benefited from its usage, especially during the time of the pandemic. Chatbots, cognitive computing, machine learning or personal assistants – these are all peripherals being widely used in the industry today. Additionally, the widespread popularity of big data, cloud computing and hyper processing systems has accelerated this.

Financial Services Chatbots and Personal Assistants have “proven themselves as a powerful tool to customer satisfaction and an unmatched resource for the enterprises helping them save a lot of time and money.” Facebook is in the process of designing bots which make negotiations the way humans do. Even supposedly menial tasks, such as organising internal or client meetings at a time when all participants are free are all being automated.

Fraud detection and management is crucial to the success of any financial services firm. And, this is where strong AI truly flourishes. It analyses past spending behaviours, different transaction instruments and flags certain oddities – such as using the same credit card in a different country just hours after it has been used elsewhere, or withdrawing odd sums of money at odd hours – all features that even humans would believe to be red flags. It also has the unique ability to learn from its mistakes – if it raises an issue incorrectly, it learns from the experience and optimises internal decisions about what can be considered fraud and what should not.

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