All About Finance and Money Laundering

Artificial Intelligence (AI) occupies a paradoxical place in our lives. For many people, understanding of AI has largely been informed and shaped by the entertainment industry, leading to concerns about dystopian futures ruled by machines; for commentators, it is often seen as the route to mass unemployment or mass leisure. At the same time, many of us have become used to the convenience provided by businesses’ use of AI across a range of sectors, from viewing recommendations to local transport.

Financial processes that have traditionally required human decision-making are increasingly being replaced or supported by AI too – in fraud detection, risk management, trading, lending and investment advice, among others.

With rapid technological developments and a ‘data revolution’ in finance, as well as pressures on the sector to cut costs, this is only set to rise.

By increasing the speed and reducing the cost of financial services, AI is celebrated for its potential to extend the provision of financial services to a wider range of people.

AI could also lead to completely new types of services – for instance, by predicting customer behaviours, AI could enable businesses to tailor services to improve customer experience (and grow sales) at an unprecedented scale. There are genuine concerns, however, that allowing machines to make important decisions about our lives using data that may only tell part of the story is a risky business.

AI Has the potential to help financial services work for customers and citizens in a number of ways:

Help people make sense of their financial habits and the best options available to them, on the basis of their financial transaction data and market information.

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This could help fill the advice gap by offering people insights, recommendations and advice regarding their finances, at scale and in an affordable way. By tracking patterns of behaviour, AI could make it easier to identify people who need help with their finances before a crisis, so that they or other organisations can take pre- emptive action to support them.

Automate actions that serve the customer’s best interest, such as transferring money across accounts to avoid overdraft fees and switching to better providers and products. Automation may also be helpful for people with mental health conditions who experience a lack of control over their spending habits and want to pre-commit to certain behaviours.

Drive competition in a way that rebalances power between customers and the finance industry. Some people think AI offers a unique opportunity to create demand-led change in finance, given that there is little evidence that financial literacy leads to improved financial management.

AI is being utilized to proactively monitor and prevent various instances of fraud, money laundering, malpractice and the detection of potential risks. For example, firms draw on

Impact of AI on Financial Transactions of the Future

individual’s spending data and behaviour to determine patterns, enabling them to identify irregular transactions. Mastercard has also worked to include AI technology as a part of their financial service network as a way “identifying identities”.

AI’s ability to deal with large unstructured data, complex mathematical models and formulas, as well as automate tasks have found applications in the securities market. Beyond wealth management applications, the potential for such technologies to further facilitate algorithmic trading is being explored.

AI technology is being used to facilitate transactions. Most significantly, it is being used as a way to secure transactions using voice recognition via banking applications. Furthermore, AI personal assistants and similar applications are being integrated with transactions in an attempt to offer a more “unified form of transaction” devoid of extra verification steps.

An Artificial Neural System (ANS) is a computer program that simulates the processes by which human learning and intuition take place. Unlike an expert system, an ANS does not rely on a preprogrammed knowledge base. Rather, it learns through experience, and is able to continue learning as the problem environment changes. The ANS is well suited to deal with unstructured problems, inconsistent information and real-time output. In particular, artificial neural systems are most effectively applied to three tasks-classification, associative memory and clustering. In the area of finance, some potential applications include assessment of bankruptcy risk, identification of arbitrage opportunities and technical and fundamental analysis.