How data science is used in fintech

Andrew Besford, Monday 30th December

Data science is now seen as an important theme in virtually every industry.  Bringing together techniques from statistics, computer science and other disciplines, it promises the potential for huge societal and economic benefits through machine learning, artificial intelligence, and predictive analytics.

Financial technology, or fintech, is a rapidly growing area which aims to innovate in and disrupt traditional ways of delivering financial services.  The UK is estimated to have over 1,600 fintech firms.

A wide range of different players in fintech are vying to create services and disrupt almost all aspects of financial services, like payments, investments, consumer finance and insurance.  Some of these are based on new internet business models, others exploring the uses of blockchain and cryptocurrencies. After an initial wave of high profile consumer-focussed fintech businesses, many in the industry are now exploring services for corporate customers and ways to build platforms that work with (rather than against) financial institutions.

The development of a fintech cluster by Dynamo North East has led many of our members to look at the ways data science can be applied to fintech.

Fintech businesses rely heavily on data science to offer new services and improve financial decision making.  And the Open Banking initiative is expected to help modernise the way that data is managed, shared and used. 

Here are some fintech themes, and the ways data science might be applied in each:

Risk Analysis – Credit rating agencies will increasingly rely on data science and machine learning to predict the risk associated with customers and identify ‘good’ and ‘bad’ borrowers.

Fraud Detection – Traditionally, the way to identify potentially fraudulent transactions has been using rules, which had to be set up manually, and might not catch emerging types of fraud. Big data and data analytics techniques can now be used to look at huge volumes of transactions and help flag or predict fraud in future transactions.

Personalisation – Companies can use data about their customers and how they use their services to create comprehensive user profiles.  These can be used to tailor the customer experience to individual users’ needs, or to provide personalised offers.

Automated Advice – Digital platforms could provide automated financial planning or automatically make investments that suit the goals of their client.  An algorithm may be able to act with no human intervention, based on information from a user profile and/or collected through an automated fact-find.

Insurance – The insurance industry is a big user of data science techniques to manage their risk. Claims departments may use data science to identify potentially fraudulent claims, or to improve automation around claims which are non-fraudulent, helping people get their money sooner.

Treasury and payments – Real-time data could be used to allow corporate treasury teams to get a clear view of their cash positions, liquidity and foreign exchange requirements. This data could then be used to help them predict how those may change over time.

Some of these examples are quite specific to fintech, and others more generally applicable, like using data to support designing new products.

To be successful, fintech businesses need to master much more than the data.  Some of their innovations will become systemically important to the financial system.  This means that central banks will expect them to meet their standards. The Bank of England’s financial policy committee recently made its toughest intervention to date, stating that digital currencies such as Facebook’s Libra would need to reach the same high standards as those of traditional payments. Many internet-based services are international which will challenge the boundaries of national regulators.

Here we’ve seen some examples of how data science might be used in fintech. Many others exist today and will be created in the future. Which of them can play a role in growing the North East tech economy?


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