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Predictive analytics in financial services

Predictive analytics is a technique that uses data to spot trends and calculate the likelihood of future outcomes. In the financial services world, insights derived from predictive analytics can minimize risk, prevent fraud, deepen customer engagement, and increase employee satisfaction.

Predictive models feed on both current and historic data, and analyzing this data involves the use of multiple tools. Experts tend to split these tools into two groups: classification models and regression models. Classification models work with discrete data, and regression models predict a continuous quality. Classification models are well-suited to binary questions or distinct values (e.g., yes-or-no questions). By contrast, a data scientist who is examining continuous qualities (e.g., price, age, height, weight) would select a regression model.

For banks and financial institutions, predictive analytics is hugely important for risk management, budget determinations, revenue predictions, customer churn predictions, cybersecurity, and fraud detection. By combing through large amounts of market data and customer data, predictive analytics models can help companies make informed and data-driven decisions, gain a competitive advantage, and identify positive or negative trends in customer behavior and engagement. Credit card companies that use risk management analytics can determine credit risk and make informed decisions about current and future customers.

Outside consultants can assist financial services companies in implementing predictive analytics solutions. Applying advanced analytics in banking can lead to revenue growth, customer retention, data-driven decision-making, and a higher customer satisfaction score (CSAT).

Analytics capabilities will continue to develop, and companies must evolve in tandem. By assuming that further developments are just around the corner, and by tracking evolutions within the financial predictive modeling space, financial institutions can stay ahead of the curve.

In employing predictive analytics, financial institutions can enjoy a number of business benefits:

  • Deeper insights from structured and unstructured data
  • Accurate predictions around customer behavior and related customer satisfaction
  • Optimization of key performance indicators (KPIs)
  • Delivery of personalized experiences to clients
  • Lowered fraud risk
  • Better understanding of customer preferences and anticipated wants
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