Competing in the crowded, dynamic financial market can be hard for companies to manage on their own. Business leaders in the financial services industry recognize machine learning can accelerate and drive digital transformation but are hesitant about how to get started.
ElectrifAi is an experienced navigator who can steer you in the right direction with pre-built machine learning models. By analyzing large data sets of disparate data, we can:
Let’s discuss the following four machine learning model use cases that have proven to be very effective for our clients in financial services.
Cross-sell means to precisely target customers by predicting what products they would be more likely to buy in addition to what they have already bought.
Our pre-built Cross-Sell machine learning model produces a score of cross-sell propensity for each customer. Using that score, you can achieve:
For example, say a customer has a stock portfolio with a financial services company. But this company also has different types of investments, products, and tax preparation services available.
Do you just shove all that information at the customer and hope something piques their interest? Or do you present one additional service or product you know they would be highly likely to purchase?
Finding an interesting service or product can be difficult for an analyst to do on their own. Sifting through thousands of data points that look at the customer’s purchase history, repayment history, demographics, and more; then, comparing that data to similar customers who have purchased the additional service or product.
That is a lot of work! Imagine being able to let a machine do the preparation work for you. Analyzing that data in a fraction of the time it would take the analyst, you can present the right service or product to the customer at the right time.
Don’t let the opportunity slip away! Or have the customer be enticed by Company Y.
Our pre-built Collections Risk machine learning model identifies early, mid, and late-stage risk and the probability of receiving payment. Then, the model also locates the Right Party Contact (RPC) so the collection outbound call actually reaches the delinquent customer.
The benefits of this model include:
For example, say a customer wants to buy a new car. They submit a credit application and are waiting for approval. The process must be fast or the customer could walk away from the purchase. But the dealership and credit issuer must also protect themselves from giving a loan to a risky consumer.
That example applies to the early stage risk detection and machine learning is a great way to quickly get answers as to whether the customer is an acceptable applicant.
Collections Risk also works well at determining those customers who are likely to default based on missed payments and will flag those customers to try remediation. If that doesn’t work, then the model also works to optimize the collections process.
Drive better engagement with your customers through our pre-built Spend Passion machine learning model. By analyzing low-utilization credit card users, you can use the data to discover spend behavior and relationships between products or services.
The benefits of this model include:
Spend Passion essentially determines what customers are passionate about in their life. Some of us may be foodies and like going to restaurants and other people are into cosmetics. Each of us has something that defines us that we’re passionate about.
If you look at a customer’s credit card history, you should be able to extract that information. But just because you spend a lot of money on something doesn’t necessarily mean you are passionate about it. That is where science comes into play.
If you take the spend data, you can generate spend signatures that identify passion from cohort spend patterns. Spending a lot of money while traveling is normal behavior and not passion.
Machine learning can help to find what products or services customers are passionate about and give you an advantage in selling that product or service.
Detecting and preventing fraud usually takes a whole department committed to auditing accounts. And yet, fraud still slips through the cracks. Our Bust-Out Fraud Detection offering is a collection of pre-built machine learning models that fill in the cracks that slip through the manual review process by using transactional history, payments, and non-monetary activity.
The benefits of this offering include:
How do we prevent fraud from happening before it even occurs? By using a look-alike machine learning model that tracks similar people who have demonstrated known fraudulent behavior.
For example, you can avoid credit loss by detecting those users who intend to rack up charges on a credit card but never pay.
This is a serious issue credit card issuers face. Preventing fraudulent charges can save a company lots of money. And using machine learning makes that process a lot easier and far more efficient.
Companies in the financial services industry can greatly benefit from the capabilities of machine learning. And ElectrifAi’s pre-built machine learning models are ready to start outputting strategic recommendations that will drive revenue, decrease cost, and reduce risk.
Do you want to accelerate your journey to machine learning? Take advantage of ElectrifAi’s experience and domain knowledge in financial services to get the most return on your investment. Contact us today for a demo!
