Successful deployment of artificial intelligence (Ai) and machine learning depend on choosing the right use cases for your company’s specific business needs. Identifying where in your business Ai could help the most will allow for that deployment to deliver real business value.
Why is machine learning so important, particularly for the financial services industry?
According to a 2021 report from Emergen Research, "the global big data analytics in BFSI market size was USD 15.65 billion in 2020 and is expected to reach USD 86.68 billion in 2027 and register a CAGR of 27.7% during the forecast period.”
Furthermore, the same study concludes that “global big data analytics in BFSI market revenue is expected to incline significantly due to increasing focus of banks and insurance companies on improving customer service, growing demand for early fraud detection in banking, financial services, and insurance (BFSI), and a rising need for faster data processing in this sector.”
Competition for customers is steadily growing as companies turn to machine learning to enhance their offerings and increase customer satisfaction. Customers are now expecting even better and more personalized experiences.
We’re going to discuss the top 4 ways financial services companies can benefit from machine learning to help meet customer demand.
Many customers today give business to companies that offer the easiest way to access information. Setting up easy-to-use apps and online portals is the key to those customers, and it is fairly standard to build. But if you want the smartest technology that can anticipate customer demands, then you need machine learning.
Self-service enables customers to do more in less time. This establishes brand credibility and loyalty as customers will be encouraged to visit the app or online portal more frequently. Customers can select how they want to interact with the technology and can quickly find the answers they seek.
Ai can also increase customer satisfaction by improving customer service. For example, most people will not wait very long to speak with a customer service agent if they’re calling to ask a question. Ai and machine learning can help make agents more efficient to answer calls faster and help more customers.
Many customers ask repetitive questions that could be put into a database. Put the question in the database (even if it isn’t the same word-for-word) and natural language processing (NLP) can find the right answers to quickly answer questions.
Machine learning can help you develop an appropriate marketing campaign so you can precisely target the right customers for your business.
There are many machine learning models we have used to help companies with their marketing efforts. Here are just a few examples:
Email Fatigue Opt-Out Prevention
Direct Mail Campaign Optimization
Email Campaign Optimization
Email Net Response
To really understand your customers and make your marketing efforts work well, you need the precision and accuracy of machine learning.
Imagine being able to quickly process each customer’s consumer banking history and behavior patterns to determine their passions. Are they interested in art? The latest technology? Travel?
Let’s say you have a credit card company that offers incentives for specific purchases, such as gas, dining out, or shopping at grocery stores. If you know the passions of specific customers, you can target them with appropriate advertising.
For example, a credit card user never purchases gas with your company’s credit card, but you know they likely have a car because they make car-related purchases. If you advertise to this user that you have a 3% cash back reward for purchasing gas, you’ve likely made this credit card go to the top of the credit card pile. And once it’s at the top, that user is likely to use it for other purchases too.
Processing credit or loan applications takes time and effort. Analysts must sift through and study all the information about the borrower, such as their credit history, changes in wages, reliability of the client, and security of the loan.
There are many people who obtain a credit card with fraudulent intentions. One of the most frequent is bust-out fraud.
Bust-out fraud is when people are issued a credit card and use it normally, paying their bills on time, etc. When the credit limit is increased, the person then racks up a bunch of charges with no intention of repaying.
How do companies prevent this from happening? By using machine learning to help prevent issuing credit to suspicious applicants.
Many companies are using customer credit application software but most of the software merely eliminates processing the applications on paper. Not only does the machine learning algorithm speed up the processing that normal analysts take a long time to do, but it gives a more thorough analysis.
Even the sharpest analysts can miss things after a long day of processing applications. The machine can confidently assure the company the applicant has a strong chance of repaying the loan.
That’s not to say the analysts are being replaced, however. Machine learning makes the analysts even sharper and more efficient. The machine can do all the heavy lifting for the analysts and flag suspicious applicants for review. That means more applicants can be processed faster, earning the company even more revenue.
Ensuring protection against fraudulent activity goes beyond processing applications. Many applicants may not have suspicious activity and cannot be flagged before issuing credit. And a lot of fraud is accidental.
All transactions leave behind patterns. Machine learning algorithms can be trained to find those patterns and flag the suspicious activity. Just as bust-out fraud can be prevented through rigorous analysis of past suspicious activity, machine learning can also be used to detect unusual credit card activity to prevent fraud from occurring for first time users.
What is the true value for organizations after implementing Ai and machine learning? This technology is displacing traditional knowledge-based authentication (KBA) methods like PINs and passwords and making it a truly powerful method to fight fraud.
Machine learning can also track purchases made at merchants with unusual patterns. Locating that merchant can prevent other people from falling prey to criminal activity, such as credit card skimmers.
For example, say 10 credit card users claim false purchases were made with their credit card. Those users can be tracked back to making purchases at the same merchant, therefore flagging that merchant as requiring follow-up.
Making better business decisions begins by choosing to implement machine learning in your company. The decision you must make first, however, is how to begin your machine learning journey.
Do you hire a team of data scientists and data engineers to build a machine learning model for you? That route can provide long-term benefits if they prove to be successful. But what happens if they spend 9 months building a model and it doesn’t provide useable results?
Or do you partner with a firm like ElectrifAi who has proven business-ready machine learning models? With 17 years of experience and deep domain knowledge in the financial services industry, we can get you the results you need to start making better business decisions fast.
Want to learn more about how your data can begin working for you? Contact us today for a custom demo.