Predictive analytics is a transformational data analytics technology that is altering business as we know it. Whether automating customer service, processing insurance claims, credit scoring, spotting fraud, or simply identifying errors, predictive analytics applications are raising the bar for intelligent, informed, and timely decision-making. Applications in the financial, banking and insurance sectors include identifying trends and patterns, optimizing planning and forecasting, and overall enhancing and elevating customer experience through expedited organizational digital transformation.
The case for predictive analytics
Predictive analytics is the science of identifying and predicting future events using historical data, current trends, statistical algorithms, AI, and machine learning techniques. Predictive analytics has gained momentum in recent years as more organizations adopt this technology gaining a competitive advantage and improving their bottom line.
With interactive and easy-to-use software becoming more sophisticated, organizations are turning to predictive analytics more than ever to help solve complex data challenges and address new untapped opportunities. Other factors like the availability of large volumes and different types of data, and having the right technology to optimally harness data quickly and cost-effectively contribute to the widespread democratization and adoption of this technology. Fundamentally, the technology allows a company to uncover important business issues or opportunities in real-time, and address them at just the right time, to achieve the best possible outcome including for areas such as
- Fraud detection – Predictive analytics enhances behavior pattern detection and helps prevent internal and external fraud within an organization. With cybersecurity becoming a growing concern, high-performance behavioral analytics sifts through networks in real-time and spots abnormalities that may indicate fraud, zero-day vulnerabilities, or any persistent threats. For instance, organizations applying advanced analytics to help prevent ticket frauds at events or identifying potentially fraudulent purchase orders.
- Revenue prediction – Advanced analytics models help attract, retain, and grow a profitable customer base as a plethora of marketing, sales, and customer behavior data makes it possible to accurately forecast revenue and anticipate future demand for products.
- Risk reduction – Predictive analytics helps create a better understanding of market movements providing deep insights and comprehensive projections. Advanced models quickly generate credit scores to assess a buyer’s likelihood of default. Other risk-related areas of impact include insurance claims and collection.
- Cross-selling – Identifying existing customer behavior patterns and cross-selling opportunities help financial institutions with their sales and marketing efforts.
Predictive analytics employs many methods and technology like big data, data mining, machine learning, and statistical modeling. This enables the financial organization to enhance and improve routine processes like
– Analyzing and identifying customers and market segments for target marketing
– Leveraging data insights gained from predictive analytics companies plan future cash requirements and avoid liquidity crisis by identifying slow payers and improving receivable management
– Establishing baseline criteria to detect financial risks and identify outliers before they damage the overall company performance
– Forecasting sales for a period predicting the future demand for the product
– Optimizing the receivables aging processes and setting alerts when customers deviate from their past payment patterns
Examples of predictive analytics
Every industry adopts predictive analytics, but there are a few high-potential financial spheres where applying this advanced analytics technology can bring significant competitive advantage and better business results.
The corporate finance industry uses advanced data analytics technology to detect and reduce fraud, and measure credit risks. From general strategic planning to agile decision-making, corporations utilize predictive analytics in their daily work including for cross-selling or upsell opportunities, and for better engagement with retail customers.
Hospitals and healthcare firms use the technology to control their finances, improve cash flows, anticipate savings, collect debts, and for improved budgeting around regulation.
Automating insurance claims processing is a significant achievement for insurers as more advanced technologies are employed. Claims are the part of the insurance lifecycle that have the highest percentage of attempted fraud and is one of many areas that companies are looking to integrate AI and predictive analytics.
This advanced technology helps the automobile industry to forecast automobile supply and demand factors. Further, it enables a better match between customer desires, experience, and pricing scenarios.
Predictive analytics in finance has unlimited potential making sense of large volumes of business data, arriving at forecasts and predictions critical for strategic decision-making. The technology is quickly transforming the world of finance and is positioned to disrupt traditional data analysis on a larger scale with a more informed AI-driven approach that provides a competitive advantage.
To learn more about how we can help you unlock your data’s potential by leveraging predictive analytics, send us an email to email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.