Predictive Analytics in Finance – Current Applications and Trends

• Teradata claims that they can build and develop enterprise level solutions where the raw data like customer information is collected, cleaned, analyzed and presented using machine learning algorithms.
• The fraud detection team at the bank can use the software’ dashboard to view alerts for anomalous transactions.
• The alerts are then investigated further by human analysts in the bank’s fraud detection team to determine if there was an instance of fraud in that particular alert event.

With the program, an analyst or bank employee can upload or integrate datasets and assign them labels such as “bill pay” or “credit card application.”

From there, according to Teradata’s Youtube playlist, a bank employee with less data science experience can then use the program to see “Paths” related to a data set. When they log on to the site, they can click the paths field and get a drop down menu with various data set labels or banking topics. The program, according to Teradata, analyzes statistics, and shows an individual’s activity through a visual image of a “path.”

This path includes labels of where a bank customer or group of bank customers’ various banking actions took place. For example, if a banker was interested in seeing what led users to signing up for a credit card, they could search for that related path on the Teredata site. The platform could generate a path saying that someone went to a credit card form, then contacted customer support, and then signed up or did not sign up for a credit card.

Teredata claims that the program can also use these paths to give a user predictive insights on other topics such as showing them paths that may signify fraud.

The 5-minute video below gives a demonstration of how Teradata’s guided analytics programs can be used to analyze online banking data:

In a case study from Teradata, the company claims that the Nordic Danske Bank used their analytics platform to better identify and predict cases of fraud while reducing false positives.

The study notes that Danske needed to find a better way to detect fraud since their traditional rules-based engine had a low 40-percent fraud detection rate and almost 1,200 false positives everyday. According to the company, over 95 percent of cases investigated were not found to be fraud.

The bank’s existing systems had similar user interaction process as mentioned for the Teradata project but with much lower rates of successful fraud identification.

According to the study, Danske implemented an “upgraded” fraud prediction and detection analytics platform. Teradata says they assisted the bank with upgrading its older machine learning models to a deep learning prediction model capable of identifying fraud in multiple channels including mobile transactions.

The case study notes that this first involved data scientists at Teradata working with employees of Danske for gathering and cleaning any existing data like customer transactions and location and establishing a ‘data pipeline’ for both existing and emerging datasets which would ensure access to the ‘right kind’ of data for the AI platform.

Teradata claims that Danske deployed their deep learning software that could generate and compare many different models for fraud detection based on data like customer geographic locations or recent ATM transactions. The model which performed the best in terms of identifying anomalies in customer and transactional data was chosen as a potential roadmap for future model iterations.

The system was not completely autonomous, Teradata noted. While it could identify anomalies in the transaction data, these detections would then have to be designated as a case of fraud by a human analyst, according to the study. For example, the platform may identify anomalies as a customer’s debit card purchases start occuring around the world, but a notified human analyst would have to investigate if this was a case of fraud, or if the customer made an online purchase that sent the payment to China followed by a purchase while vacationing in London.

After a five month setup and integration period Teradata claims that their deep learning model was able to perform significantly better than Danske’s existing rules-based engine and machine learning model in terms of reducing false positives in the anomalies detected. The figure below shows company results of the product:

Reducing false positives may be an important way some companies can enhance their user experience. When customers do not have to worry about their legitimate transactions getting recognized as fraudulent, their engagement with the company’s brand may become more amicable than before We spoke with Sebastien de Brouwer, Chief Policy Officer of the European Banking Federation, about where business leaders should be focused in terms of AI on our podcast, AI in Banking. When asked about which capabilities will matter in terms of being critical in the future, de Brouwer said,

“We strongly believe that AI will have indeed a transformative effect on the banking industry … The most important aspect is certainly that it will change and hopefully enhance the customer experience. So that’s already a very important element I think for the banks who will succeed, and that is of course interactivity with the clients, because this should allow [banks to use interactivity data to create better offerings.] … One activity where many banks are looking at is investment advisory or recommendations. I think that is certainly an area where no big players are looking very seriously at AI [as a solution.] … This may also expand the client segments that would have access to those kinds of services.”

Along with Google alum Ron Bodkin’s experience, the team’s Principal Data Scientist, Jack McCush previously earned a Master of Arts in Statistics and a Dual Masters of Arts in Economics and Statistics from the University of Missouri-Columbia.

Teradata also claims to have worked on projects with companies like Maersk Line, Verizon, Siemens and Proctor and Gamble.

Managing Credit Card Default Risk

DataRobot

DataRobot is a Boston-based startup founded in 2012. The 400+ employee company claims to offer predictive analytics services in the FinTech space through its Automated Machine Learning platform.

When a bank employee or lender logs in, they see a data dashboard showing columns and cells with key aspects that they would like to monitor. They dashboard is also capable of showing insights and trends in various graph formats. A user can also search and look at specific data associated with someone applying for a loan and his or her loan application to determine if they should get approved.

According to DataRobot, its services aim to predict risk in lending (credit default rates) or identify anomalies in payment transactions for fraud detection. For example:

• The Bank of America (BofA), one of DataRobot’s clients, might lend money to customers in the form of loans or credit cards and growing their business means increasing the value and number of such loans. The bank may need  a scalable strategy to predict the likelihood (risk) of default among large numbers of applicants.
• The bank’s loan managers might use the DataRobot platform to gain insights on risk of loan defaults for new customers by means of a dashboard.
• The AI platforms are trained using historical loan repayment records and other data like social media data to coax out patterns that might lead to a customer defaulting on credit card payments. DataRobot claims that their platform can also clean and parse the raw data although users can also use third party data cleaning tools like Trifecta (see video below)
• Loan managers can then use the dashboard to review the applications that have a high risk of default thereby speeding up the loan approval process.
• BofA might use DataRobot’s predictive analytics platform to predict the risk of default for new borrowers by analyzing historical data about existing borrowers default rates. By integrating these predictive models into their loan-approval the bank could potentially expand their loan portfolios while simultaneously managing the risk involved, according to DataRobot.

Below is a 1-minute video which gives a demo of how businesses can leverage their internal data using DataRobot’s Automated Machine Learning & Predictive Modeling Software:

From our preliminary research we found several use-cases from DataRobot of predictive analytics in FinTech applications.