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All Journal International Journal of Multidisciplinary Sciences and Arts Jurihum
Latha Narayanan Valli
Vice President, Standard Chartered Global Business Services Sdn Bhd., Kuala Lumpur, Malaysia
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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A succinct synopsis of predictive analysis applications in the contemporary period Latha Narayanan Valli
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i4.4625

Abstract

A potent subset of data analytics called predictive analytics is revolutionizing a number of industries by using historical data, machine learning methods, and statistical algorithms to predict future events and guide strategic choices. The uses and advantages of predictive analytics in the fields of finance, healthcare, manufacturing, energy and utilities, retail, and marketing are highlighted in this thorough overview. Predictive models improve market risk management, fraud detection, and credit risk assessment in the financial sector, promoting stability and confidence. Applications in healthcare include operational efficiency, tailored treatment, and patient risk assessment, all of which improve patient outcomes. Supply chain risk management, quality assurance, and predictive maintenance all help manufacturers maximize efficiency and reduce downtime. Demand forecasting, asset performance management, and regulatory compliance all help the energy and utilities sector by guaranteeing dependable and effective service delivery. Predictive analytics helps retailers satisfy customer requests and keep a competitive edge by assisting with inventory management, customer satisfaction, and competitive analysis. Customer segmentation, personalized marketing, campaign optimization, sales forecasting, churn prediction, customer lifetime value prediction, market trend analysis, and sentiment analysis all greatly improve marketing techniques.
A succinct synopsis of predictive analytics for fraud detection and credit scoring in BFSI, Latha Narayanan Valli
JURIHUM : Jurnal Inovasi dan Humaniora Vol. 2 No. 2 (2024): JURIHUM : Jurnal Inovasi dan Humaniora
Publisher : CV. Shofanah Media Berkah

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Abstract

The Banking, Financial Services, and Insurance (BFSI) industry is undergoing a change thanks to predictive analytics, which uses statistical methods and machine learning algorithms to predict future trends and probability. An overview of the main advantages, anticipated developments, difficulties, and factors to be taken into account with predictive analytics in BFSI are given in this abstract. Improved risk management, better decision-making, more customer satisfaction, and operational efficiency are some of the main advantages. Future trends include improvements in real-time processing capabilities, growing usage of big data and IoT, and developments in AI and machine learning. Ensuring data quality and regulatory compliance are challenges, while ethical data use and model interpretability are problems. To fully realize predictive analytics' potential in BFSI, success in the field necessitates resolving obstacles, embracing emerging trends, and maintaining moral principles.
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