Another week, another set of interesting A.I. news articles I read.

One question I often get is the practical use cases of A.I. or Machine Learning. When people hear the term “artificial intelligence”, they often think of out-of-this-world algorithms hell-bent on destroying our world or at the very least determined to render us jobless. For the most part, this is not the case because our world is still spinning yet we interact with dozens of these algorithms every single day and sometimes without being conscious of that fact. I will write an article discussing how many A.I. algorithms you interact with on your daily commute to work, sitting at your desk grafting, stalking your crush on social media etc and what they are. For today, let us focus on use cases in Banking.

1. One assumption people have is for a company to apply A.I. algorithms, it needs to be well-resourced with a Fortune 500 level balance sheet etc. While in some particular instances this is true, many players in this field are open-sourcing their projects and anyone can easily tap into this knowledge to create solutions for any size company. In this article by Marc Butterfield, the Vice President of Digital and Payment Solutions at First National Bank of Omaha, talks about some use cases of A.I. in banks no matter the size. He says, “There are several reasons why I’ve come to believe the AI leap will yield financial returns for any bank, no matter how big or small. The first is that credit underwriting has historically been a blunt instrument. The FICO score, which measures credit risk, is inherently a backward-looking metric that takes into account past behavior. What is unique to AI is the ability to process a large number of signals from the credit report and alternative lending data that, taken together, can reduce risk, expand access to credit and lower the overall interest rate that someone pays on a loan.” His article talks about how A.I. can be leveraged in the lending process especially around using alternative lending data in conjunction with the traditional sources, improving processes using digital and helping brands maintain their brands in the eyes of their clients (marketing): https://www.americanbanker.com/opinion/small-and-mid-sized-banks-cant-shy-away-from-ai

2. Personally, I consider bankers who sit on our trading desks to be superheroes. There is a lot happening at any one given time and I don’t know how they keep up with everything. The volatility can swing either way and if it goes in their favour, they can get themselves a share of the US$5 trillion-a-day global foreign exchange market. To help their bankers and their clients keep up, UBS introduced a machine learning algorithm they called ORCA-Direct. According to UBS, it “learns in real time, utilizing historical trading data to find the bank’s clients the best available liquidity when volatility rises.” Since launching the algorithm, the bank’s forex businesses doubled in 2018. Machine learning helps the algorithm to “determine within microseconds the best platforms and execution sequence to use, estimating the probability of trading and market impact for each specific order and reducing the costs for the bank’s clients.” https://www.reuters.com/article/us-ubs-group-investment-bank-digital/ubs-looks-to-machine-learning-to-plug-fx-liquidity-gaps-idUSKCN1SK1CG

3. It’s not only banks in the Western world who are leveraging machine learning. Banks in India are doing the same. Like in every country in the world, banks there are working on reducing costs, meeting margins and exceeding customer expectations through personalization. This is where A.I. is coming in. Various banks in India are implementing A.I. algorithms in customer services, fraud and risk management, trading and securities, credit assessment, and portfolio management. If you have been wondering where A.I. or machine learning can be used in a bank setting, this article will definitely get you excited and get your juices flowing. For each one of these use cases and more, I will also publish articles digging a bit deeper into each use case to provide you more detail and especially contextualize it South Africa: https://yourstory.com/2019/05/how-artificial-intelligence-changed-banking-sector

Within the next coming weeks, expect articles where I will provide some use cases that are relevant to us here in Africa. And I am open to discussions around this. Feel free to message me back if you have any question or there is something you are thinking about around this. Until next time,

It’s a brave new world!