Benefits of Predictive Analytics in Finance Sector
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Did you know? According to Statistica, the predictive analytics software market is expected to grow to over 41 billion U.S. dollars by 2028. What does that mean to financial institutions? Apart from retail and healthcare, banks and financial institutions process enormous amounts of data on a day-to-day basis, ranging from customer, transaction, and application data. All the data can be a game-changer in predicting future outcomes with the help of predictive analytics. Financial institutions that adopt ML models in their operations can accurately forecast their financial goals and customer behaviors (new and old) and make informed decisions to boost their revenue.
Are you a decision-maker at a financial institution looking forward to employing ML models? Here you go! Below are some successful benefits of predictive analytics in the finance sector.
1. Loan Default Prediction
Challenges Faced: Banks have been concerned about high default rates, which result in substantial financial losses and affect credit portfolio quality. Traditional credit scoring models track only limited data points, inaccurately reflecting the borrower’s risk in volatile economic conditions.
Predictive Analytics Benefits
Predictive analytics leverages a broader data set, including transaction history, spending patterns, social media activity, and behavioral data. Advanced machine learning algorithms analyze these diverse data points to create more accurate and dynamic risk profiles. Predictive analytics enables banks to make better-informed lending decisions, set appropriate interest rates, and implement targeted risk mitigation strategies.
2. Churn Prediction
Challenges Faced: Identifying customers at risk of leaving is challenging for financial institutions, as the reasons for churning can vary, ranging from service dissatisfaction to better offers from competitors. Besides, customer attrition directly impacts on a financial institutions’ revenue and market share.
Predictive Analytics Benefits
Predictive models can identify early warning signs of churn by analyzing service usage patterns, customer transaction history, engagement levels, and feedback. With this information, banks can proactively address the issues by offering personalized discounts and enhancing customer engagement through targeted messaging, thus increasing customer retention rates.
Read our Work: Minimized non-performing assets to a century old bank in Asia
3. Fraud Detection
Challenges Faced: Fraudulent activities such as identity theft, account takeover, and transaction fraud pose significant risks, especially in financial institutions. In contrast, traditional fraud detection systems struggle to keep up with new fraudulent activities, resulting in undetected fraud and false positives.
Predictive Analytics Benefits
Since predictive models continually learn from new data on a day-to-day basis, identifying anomalies and unusual patterns that could indicate fraud is easier. These models analyze user behavior, transaction data, and other relevant factors to flag the most suspicious activities in the system with a prior alert. This function enables accurate and quicker detection of potential fraud, enhancing security.
4. Cross-selling and upselling
Challenges Faced: Identifying the right product or service to offer to the appropriate customers at the right time is a smart move to maximizing revenue. However, without deep diving into customer preferences and needs, banks and financial institutions can miss out on up-selling and cross-selling opportunities.
Predictive Analytics Benefits
Predictive models assess transaction histories, customer profiles, and product usage to find patterns and predict future needs. For instance, a frequent air traveler may need a customized travel credit card. By considering such insights, banks can personalize their offers to each customer, boosting overall sales.
5. Customer Service Optimization
Challenges Faced: With the advancement of digital channels such as mobile apps, online banking, chatbots, and social media, banks ought to provide seamless service experiences across multiple touchpoints. Maintaining consistency in offering customer service across channels can be complex for financial institutions.
Predictive Analytics Benefits
Predictive analytics models can forecast future customer inquiries, service demands, and interaction volumes based on seasonal trends, historical data, and market dynamics. By accurately predicting service requirements, banks can optimize staffing levels, allocate resources effectively, and reduce wait times.
Interested in knowing more about employing predictive analytics in your financial institution? Talk to our AI expert.
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