The credit card industry has to deal with a lot of challenges. One of the most significant is credit card fraud.
There was a 140% increase in US eCommerce fraud in 2020. Global losses to banks and merchants surpassed $28.58 billion in the same year. Experts predict that by 2025, the figure will have topped $43 billion.
Another significant challenge is credit risk. In the US, the credit card debt stands at $856 billion. This signifies a $52 billion increase from the last quarter of 2021. Dependence on credit cards to meet daily expenses poses a significant credit risk.
Data analytics can help with credit card fraud, risk, and other areas within the industry. Big data is the buzzword nowadays. It incorporates the process of sifting through tons of raw materials.
Relevant technologies help with analysis. Out of this, the credit companies get actionable insights. The insights are critical for decision-making and better business practices.
Our article explores the potential of data analytics in the credit card industry. It has a lot to offer, as we will share.
Role of Data Analytics in Credit Card Companies
Let’s start by saying the credit card industry has tons of data analytics jobs. If you are looking for one, signing up to the right platform will get you nearer your goal. You receive timely notifications any time a suitable position opens up.
So why are we comfortable making such a statement? Well, credit card companies collect and manage tons of data. They need people with skill and talent to manage such tasks.
A lot of work will go into capturing data, analyzing, and gaining insights. And, even with automation, there is still a significant need for human talent.
Data analytics has many benefits to a credit card company. We can summarize them as follows.
- Reducing Credit Risk with Analytics
Data analytics provides a tool for understanding customers better. Credit card companies must understand their needs to respond with the right solutions.
Data analysis can also tell credit card companies who is likely to default on payments. AI and ML technologies look at the behavioral patterns of customers. The software looks at trends like spending or buying behavior to provide feedback.
It also factors in the payment history and earnings of potential and present customers. These insights help in deciding whether to offer credit facilities.
A customer who cannot keep up with repayments may have the same issues with their credit cards. Extending credit facilities to such a customer poses a high credit risk.
- Fighting Fraud with Data Analytics
Pattern recognition and neural networks look at patterns within individual accounts. As we stated above, the algorithm takes note of past spending behavior.
But that’s not all. It also looks at factors like IP address, time, and even type of device. A series of transactions from many locations could be indicative of fraud.
Credit card companies can detect any transactions that seem suspicious. A sudden change in purchasing habits could raise a red flag.
Many expensive purchases from a moderate spender is an example. The companies can stop further transactions while notifying the customers of the same.
The same applies to predictive analytics. Such insights identify fraud patterns before they cause any damage.
Machine learning ensures the prevention or reoccurrence of fraud threats. The strength of such technologies is their accuracy and reliability.
Data processing algorithms have helped reduce losses for credit card companies. The challenge for the companies is to improve the analytic programs. False positives can lead to the cancellation of a transaction. Yet, it is the card owner who is making it.
A moderate spender may, for once, decide to splurge on an expensive item. The algorithms may determine this as a fraudulent transaction since it is out of the norm. Refining the algorithm is a challenge that the industry needs to tackle.
- Data Analytics for Marketing
The insights from the data analysis are critical for marketing. Customers with good repayment histories are excellent for product targeting, campaigns, or incentives.
Understanding customers’ spending habits lets the company know where to focus its efforts. A customer who travels a lot would be a good candidate for travel-related products.
Knowing where they spend their money opens up other opportunities. Partnering with their preferred travel company can open up areas for more spending.
And that’s not all. With data analytics, there is a greater opportunity for personalization. American Express, for example, allows customers to add brands onto their cards.
They will then receive personalized offers from partner companies. The customers also get a rebate or cashback from select retailers.
- Business Growth and Expansion with Data Analytics
Business intelligence cannot exist without data. The credit company depends on analytics for critical decision-making. The data Insights can show factors like performance and areas that need improving.
Analytics are important for pricing, customer retention, predictive sales, and more.
Through research and data analysis, the company has an opportunity to expand. They can identify untapped areas and optimize on such.
The insights will reveal areas of need amongst consumers. The company could get this from questions, or feedback customers give. They can then tailor specific products to meet the demand.
Final Thoughts
Data analytics has a lot of potential in the credit card industry. Companies are already using it for several tasks. Fraud detection, for instance, is one of the biggest beneficiaries. The algorithm can detect patterns of behavior that are suspicious.
AI and machine learning are critical tools for collecting such insights. They dig deep by looking at past spending behavior. They can then predict future patterns. The tools also help with service personalization and better targeting.
Data analytics also plays a significant role in reducing credit risk. A look at a customer’s history will show whether they are likely to default on a payment. The company can use such insights to determine whether to extend credit facilities. Some customers are not worth the risk.
Data analytics also helps credit companies improve their service offering. By understanding customer needs, they can tailor-make solutions that respond to such gaps.
