4 Ways Financial Firms Put Machine Learning to Work

 

Several industry giants in the finance sector are well on their way to implementing machine learning technology that improves operations and guides strategy in multiple departments. So far, machine learning algorithms are being used in this industry to detect fraud, supplement traditional credit scoring, evaluate loan applications, predict customer churn, automate communications to serve customers more efficiently, and identify stock market trading patterns. 

Some firms are already saving hundreds of thousands of labor hours annually with their machine learning endeavors, while other firms have yet to deploy models. Right now, machine learning is still a “nice-to-have,” but it will soon be a must-have to remain competitive. You may have had some conversations around implementing a machine learning program, but where should you start? Here are four common applications of machine learning in the financial sector that have been implemented with open source technologies: 

 1. Fraud Detection

One of the most common applications of machine learning in the finance sector is fraud detection. Fraud detection algorithms can be used to parse multiple data points from thousands of transaction records in seconds, such as cardholder identification data, where the card was issued, time the transaction took place, transaction location, and transaction amount. To implement a fraud detection model, multiple accurately tagged instances of fraud should already exist in a data set to properly train the models. Once the model detects an anomaly among transaction data, a notification system can be programmed to alert fraud detection services in the moment the model identifies a suspicious transaction.

Fraud detection is a type of anomaly detection algorithm. These algorithms can also be applied to data sets in other areas of the company to serve different purposes, such as network intrusion detection. This is one reason why some companies find more value in investing in an enterprise data science platform, rather than purchasing out-of-the-box models or pointed analytic solutions.

 2. Credit Scoring

By implementing credit scoring algorithms, financial institutions do not have to rely on generic scores from the top three score reporting firms in the U.S. Many lending institutions see the benefit in developing custom credit-scoring models that utilize the institution’s own customer activity data to better predict the risk or opportunity of extending a new line of credit. By doing so, they can reduce delinquency costs that come from loan write-offs, delayed income from interest and the servicing cost of trying to collect late payments.

To maintain the most accurate credit scoring, customers are continuously re-evaluated as new data is obtained about missed payments or new debt. Machine learning algorithms are used to update these scores as new data rolls in. Credit scoring algorithms are essentially predictive algorithms that should be trained using data from past loans, granted there is enough data from both good and bad loans to train them effectively. These predictive algorithms can also be utilized at the macro level to assess risk and predict market movement. 

 3. Natural Language Processing for Contracts

 Many natural language processing (NLP) algorithms have been developed using Python in recent years. For financial institutions, NLP algorithms can be trained to read and parse contracts, reducing hours of redundant labor. JPMorgan developed such a text-mining solution they refer to as COIN (Contract Intelligence). COIN helps analyze commercial loan contracts by parsing the document for certain words and phrases, saving the company 360,000 hours per year.

4. Natural Language Processing for Customer Feedback

NLP models are built and applied to customer communications on social media, phone transcripts, and customer service chat platforms. NLP can be used to analyze comments for sentiment and intentions. With NLP, machines can categorize customer feedback to help banks and financial institutions better understand the overall sentiment of customers, what the majority of complaints are about, and glean patterns to recognize areas for improvement. They can also be trained to identify patterns in problems before they turn into large-scale issues that affect a larger number of customers.

Keeping up with Innovation

Cutting-edge machine learning algorithms are almost always created in the open source community (like visual and audio data processing). The sheer pace of innovation in this community makes access to open source packages and libraries indispensable for data scientists. Organizations that are looking to enable and execute on use cases like the ones mentioned here are now taking what they know and love about the open source community and empowering their data scientists to collaborate and deploy using a centrally managed, scalable open source data science platform. This allows them to adapt models to their unique business cases and scale from a few models to hundreds of data scientists with thousands of models.


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