5 Applications of Machine Learning To Improve Customer Experience in the Credit Industry


Machine learning is not an unheard concept for most of us, after all it’s 2018 and popular culture alone has theoried a plethora of post-apocalyptic timelines where machine learning runs amuck. The more pedestrian (though still exciting) reality is the rapidly expanding and unique applications of large-scale data analytics to create dynamic and predictive computer models.


A pioneering process of analysing and automating data that gives computer systems the ability to ‘learn’. Using complex algorithms that frequently learn from raw data and information, machine learning gives computers the ability to discover hidden insights without being specifically programmed on where to find them. The massive data sets used for machine learning make it possible for computers to extract both patterns and anomalies from the torrents of information. It’s an evolution of Artificial intelligence practices such as pattern recognition and computational learning theory, machine learning allows programmes to make faster, more accurate data-driven decisions.

Okay, so it’s making computer processes more efficient, reliable and cost effective, this becomes a recipe for explosive growth as it translates into a number of tangible bottom line benefits for any business that interfaces with customers (which is pretty much all of them). The advent of machine learning technology is driving innovation in every sector and will only continue to grow in popularity as it progresses. Big brands have already bought into the machine learning era decades ago when it was fledgling, the potential was always there for data driven artificial intelligence to offer their customers an optimal experience. Apple use machine learning for their voice recognition system, Siri, to imitate human interaction. Facebook use the technology to tag individuals in photos. Google Maps analyses the traffic speed using location data from smartphones. PayPal uses machine learning algorithms to combat fraud. Amazon gets you to spend more money by showing you more products it knows you want to buy based on your history because it’s machine learning algorithms have learnt a lot about the average joe over the last 2 decades.

In the credit industry specifically, brands like Wonga use a business model where machine learning plays an integral role in assessing customer risk and approving loans. So far, it has mainly been used for determining the lending risks of customers based on their income and credit history, but there is much more potential to still be tapped. Machine learning allows credit companies to offer a more enhanced customer experience in a number of ways:

  1. Call Reason Predictions:

Call reason predictions are a new and intuitive way for machine learning to make a huge impact on the credit industry. The number of customer calls being made on a daily basis is always increasing, and as a result the number of calls being routed to incorrect departments is an increasing issue. Customers are also experiencing longer wait times than ever, leading to an increase in call drop-offs and unsatisfied customers. Machine learning can help ease this issue by predicting the reason for a customers’ call based on the time of day the call is being made and other variables. This allows the call to be routed directly to the correct department for that specific customers’ needs, resulting in shorter calls and happier customers.

  1. Chat Bots:

Machine learning can also be used to assist customer without them having to pick up the phone, with the rise of chat bots and conversational interfaces. Chat bots are virtual assistants that are built using natural language processing engines combined with credit specific customer interactions. Allowing customers to get information and interaction quickly and easily will improve service levels and offer a unique edge over other companies that require their customers to get in touch using traditional methods. As well as improving the customer experience, these virtual assistants save valuable work force time, allowing businesses to save money and have their teams focus their time on other aspects of the business.

  1. Fraud Detection:

With the combination of computing power, the internet and an increasing amount of valuable and confidential data being stored online, the security risk to data is higher than ever before. Previously, financial fraud detection systems were heavily dependent on complex and robust rules. Whilst more recent fraud detection goes further than just following a checklist of risk factors, it can actively learn and calibrate to detect new potential security threats. Machine learning systems can detect anomalies in online activities and behaviours and flag them for security teams to investigate further.

  1. Product Sales:

Machine learning is a powerful tool for developing more detailed insights into customers and potential sales prospects. Drawing on a wide range of available data, marketers and sales staff are given much more to work with than they have ever had in the past. Customers can be more accurately segmented according to their profiles and probable needs, offering new opportunities for up-selling and cross-selling. This in turn improves the customer experience by offering them the exact products that are relevant to their needs.

  1. Collections:

Currently some customers have very negative experiences from credit companies due to being contacted by their collections teams unnecessarily. Machine learning can help ease this issue by learning and predicting which borrowers that go into arrears are more able to resolve their issues without contact or support from the credit company. Not only will this improve the customer experience by avoiding unnecessary hassle, it will also have an impact on reducing the cost and time of collections teams.

The credit industry has long had access to an enormous volume of accurate historical records and personal data, making it the perfectly suited for machine learning optimisation. Expect to see a huge increase in the amount of machine learning applications being applied within the industry in the near future, indeed expect to also see it become the mainstream foundation for the marketing and customer service strategies of even small business owners across the web. As the availability of user information nears 100% (do you know anyone who doesn’t use a smartphone now?) the investment into this sector will continue to grow as countless applications and valuable, market dominating insights about our customers await realisation.