Analytical Engine for Customer Behavior Analysis and Financial Service Recommendations.

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Analytics - Developing improved financial forecasts and results analysis to enhance decision-making.
Company: Financial Services Co. | Location: UK
Technology: Azure ML, Azure AI, Azure ML Studio, Azure Power Automate, Azure Cognitive Services.

Project Background

A leading UK-based financial services enterprise sought to create advanced analytical financial dashboards to better understand revenue forecasts and offer personalized financial planning recommendations to customers, enhancing customer satisfaction. The company aimed to leverage AI to cut operational expenses (OpEx) while automating processes to reduce manual intervention and decrease workflow duration by 50%.

Challenge

  • Managing vast amounts of data and producing accurate analytics required a redesign of existing data models to support anomaly detection.
  • Many processes were prone to human error and caused delays in service delivery, necessitating automation.
  • Financial projections were being made using traditional methods with little to no advanced analytics to anticipate future trends.
  • The company needed to build an AI-driven recommendation engine to analyze customer spending patterns and provide tailored banking services to improve customer satisfaction.

Perennial’s Implementation

  • Conducted a comprehensive assessment of the data infrastructure to determine the feasibility of implementing AI and ML on the existing data models.
  • Re-architected the Azure PaaS-based technology stack, utilizing Data Lake and Data Warehouse Analytics to optimize data processing time.
  • Leveraged Azure ML Studio to develop data algorithms trained on historical financial transaction data, customer spending patterns and revenue data.
  • Integrated Azure Stream Analytics and Power Automate to establish real-time data analytics, enabling live recommendations without manual intervention.
  • Implemented Azure Cognitive Services to analyze large data sets, detect potential fraud and report anomalies.
  • Engineered a scalable data architecture to support machine learning for near-accurate revenue projections, customer service recommendations, growth analytics and predictive fraud detection.

Key Highlights

  • Developed an AI and ML-based customer recommendation engine, enabling self-service capabilities for personalized banking services.
  • Delivered an intuitive UI/UX for data insights, empowering stakeholders to make informed decisions on financial planning.
  • Achieved a 60% reduction in processing time and streamlined banking operations through automation, leading to higher customer satisfaction.
  • Automated processes that complied with regulatory standards, reducing upgrade times by 70%.
Success Story

Transforming Insurance Applications with AI for Efficient Fraud Detection.

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