ML/AI in Fintech
Machine Learning is one of the many applications of Artificial Intelligence, which enables systems to automatically learn based on data and experience. It is based on the premise that computers can learn through being exposed to data, and can adapt and improve as more data is made available, without being explicitly programmed. This allows them to learn how to recognise patterns and produce reliable decisions. While machine learning may initially have been applied in games such as checkers, today machine learning is being used across a range of applications – from self driving cars and chat-bots to fraud detection software and money management tools.
One particularly interesting area to look at is the application of machine learning in fintech. Fintech revolves around the usage of cutting-edge technology in the world of finance, enabling greater financial inclusion, and lower costs. What better use of machine learning can there be than this? Let’s talk about a few areas in fintech where machine learning can particularly add value.
Credit Risk Profiling
With millions of potential borrowers being unserved by the traditional financial industry, machine learning and artificial intelligence can play a large role in assessing the creditworthiness of the unbanked. These individuals are unable to build a strong credit score based on traditional measures.
However, applications like Lenddo and Afirm use a range of non-traditional data and artificial intelligence to assess the probability of loan repayment and an overall credit risk score or profile. This includes social media data, web browser history, geo-locations, and even POS data in some cases. Based on this, financial institutions can determine the loan amount and terms to be provided.
Using historical data of fraudulent, as well as regular transactions, machines can learn to recognise patterns of suspicious behaviour, and trigger warnings accordingly. Artificial intelligence can help identify implicit and hidden correlations in data, and learn through new data to help identify risk areas which would have otherwise been missed.
This not only helps save financial institutions the cost of fraudulent transactions themselves, but also helps save man hours wasted in manual monitoring and detection, and costs of recovery and customer service. A report by LexisNexis suggests that for every $1 of fraud, companies have to spend $2.8 to $3.6 (varying by industry and the scale of company) to resolve the problem – all of which can be saved through effective fraud prevention.
Trading and Money Management
A recent survey by the financial research firm, Autonomous Next, demonstrated that only 34% of people would be comfortable following machine-led financial advice. Yet, we are not far from the point where machine learning will be used to make investment decisions and manage portfolios.
Computational technology today has caught up, and decisions made through machine learning and deep learning can outsmart human decisions, and with more efficiency. Already, CitiGroup is using artificial intelligence to make portfolio recommendations to clients, whie PanAgora Asset Management uses complex algorithms to test investment ideas.
By 2030, machine learning and artificial intelligence is expected to save the banking industry more than $1 trillion. This includes savings gained from all areas work, trough application of chat bots, fraud prevention tools, credit underwriting, identity verification and KYC, and asset management. We are indeed not far from the world where machines will be making decisions for us.