Top 5 Use Cases for Machine Learning in Finance
Many companies in the financial services industry are benefiting greatly from artificial intelligence (Ai) and machine learning capabilities. According to research by The Economist, the benefits of greater Ai adoption are widely recognized across the financial services industry, including reduced cost base and better predictive analytics. To get ahead of the competition, you must begin your own digital transformation.
Advances in Machine Learning for Finance
Legacy systems are not as effective in today’s market. They prevent the adoption of advanced technologies that can help companies beat the competition. Ai can help reduce cost and provide amazing predictive analytics that can turn your data from stagnant waste taking up storage to valuable insights.
These insights can create a better customer experience and increase satisfaction. And happy customers are less likely to jump ship to competitors who have great reviews and offers. Encourage your customers to remain loyal to your brand with machine learning.
Taking machine learning in finance from theory to practice can be done more quickly today than in the past, thanks to these technological advancements. With better tools for data science, DevOps and machine learning you can achieve faster compute (processing) speeds, more available data, and better algorithms.
How Machine Learning Can Be Used in Finance
For the last 17 years at ElectrifAi, we have worked with organizations to apply machine learning in banking and finance. Here are the top five use cases for machine learning in finance:
1) Fraud Prevention
Money laundering techniques can be prevented by manual financial monitoring. But manual monitoring alone can only do so much. Machine learning models can help you detect more instances of intentional or accidental fraud. Build a deeper level of defense to catch more fraud and reduce false positives with accurate results.
Machine learning can also target fraud even before it happens by learning from patterns of normal behavior. Check out this resource to learn more about how ElectrifAi is fighting fraud with machine learning.
2) Contract Management
Companies in the financial services industry, particularly insurance, process many contracts each day. Lots of effort and entire departments are put to work manually processing and reviewing these contracts.
With the power of Ai and machine learning, contract management enables humans to better strategize and make informed decisions. Recognizing patterns in the contracts, keyword searches, analysis of terms, and much more can be utilized to a company’s benefit, such as negotiating better terms and conditions.
3) Process Automation to Replace Manual Work
Machine learning does not replace jobs; rather, it makes employees more efficient at their jobs. It also increases employee happiness by removing tedious, boring parts of a job in which that time could be put towards more productive efforts.
It takes time to manually process data with an increased chance of creating errors. In fact, “there is a 90% chance of a logic error for every 150 rows in the [Excel] workbook.” Automating manual processes helps you focus on tasks for more accurate results, reduced time and costs, higher efficiency, better customer experiences, enhanced compliance, etc.
4) Customer Data Management
Customer data management (CDM) helps businesses keep track of their customer information and survey the customer base to gain direct feedback. Many companies suffer from data inaccuracy, which means those data-driven companies rely on poor data to make decisions.
With machine learning, you can eliminate the headache of unorganized data and create a solid persona for each customer. This persona can be used by marketing to send personalized offers and create an excellent customer experience.
5) Predict Customer Behavior
Understanding your customers is crucial to achieving business success. For the financial services industry, offering lines of credit to customers who cannot repay the loan is detrimental. In fact, research from the Data Driven Investor shows that to reduce the credit risk associated with borrowers, a credit analysis should be completed to reach a lending decision.
Manual credit analysis, however, can take much longer than customer behavior machine learning models to produce results. By predicting customer behavior, machine learning can help companies understand which customers to offer a line of credit.
Using data companies already have (e.g., demographics, purchase history, repayment history, etc.) the machine learning model can help companies make solid decisions regarding customers and their ability to follow through on their obligations to repay the loan.
Ai Models for Finance
Implementing machine learning into your business is a great strategic decision. Yet, many companies choose not to because of cost or the time it would take to start producing results. Research from The Economist states the largest perceived barrier to wider adoption of Ai is cost.
Did you know that machine learning doesn’t have to be as expensive or take as much time as most people think it does? At least, not when you partner with an experienced firm like ElectrifAi.
Our pre-built machine learning models are ready to take your business to the next level. Financial services companies can really benefit from this technology because of our deep domain knowledge.
We can fit the models to your exact business requirements using your own data, not a theoretical data set created in a lab. Our models have been used in real-world situations and proven to provide a great return on investment (ROI).
Learn how ElectrifAi is using ML to help credit card providers reduce risk and improve customer experience. If you are ready to find out how machine learning can help your company, contact us today for a custom demo.
 Pareto's 80/20 Rule for Corporate Accountants, by David Parmenter, John Wiley & Sons, 2007, p. 149.
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