Automate
❯
Automate Process
❯How ML Can Improve Churn Prediction to Retain More Revenue for Insurers
Large Companies
NLP - Text summarization
For:
Media Intelligence Analysts
Scope:
Using Abstractive Text Summarization to reduce analysis costs for media monitoring.
Goal:
Improve Operation Efficiency
Intelligent Document Processing Using AI
For:
Government & public entities
Government departments
AI-Based Solid Waste Classification
For:
Government Municipal Corporations, Solid Waste Management Companies
Large Companies
Small Companies
Entertainment and Media - Subtitle Creation
For:
Content creators
Scope:
Creating efficiencies for content creators via automatic subtitle creation for social video.
Goal:
Improved Employee Efficiency
Large Companies
Small Companies
Improve Business Decision
For:
Credit controllers
Scope:
Using Machine Learning to automate the assessment of creditworthiness for loan applicants at a bank.
Goal:
Improve Operation Efficiency, Increase Revenues
Intelligent Social Listening
For:
Local authorities, Government agencies
Automated Quality Assurance
For:
Quality Engineers
Large Companies
NLP - Machine translation
For:
Translation Managers and Translators.
Scope:
Using Machine Translation to scale content production and create a more streamlined and efficient translation process.
Goal:
Improve Operation Efficiency, Automate a Business Process
Procurement - Cost Analysis
For:
VP Global Supply Chain Management, Category Managers
Scope:
Realizing operational efficiencies and working capital improvements through automated spend classification.
Goal:
Reduce costs, Improved Employee Efficiency
How ML Can Improve Churn Prediction to Retain More Revenue for Insurers
For:
Portfolio Managers
Customer Retention
Goal:
Improved Operation
From Weeks Down to Hours: How insurers innovate with AI to shorten claims processing time and improve customer experience
For:
- Claims Management
- Customer Experience.
Goal:
Improved Customer Experience
General Public
Education - Smart Learning Content
For:
Online course creators and online learners.
Scope:
Empowering course creators to focus on complex decision-making and creativity with Computer Vision and Natural Language Processing.
Goal:
Improved Employee Efficiency
Large Companies
Accounting and Finance - Improve Profitability Reports
For:
Financial analysts
Scope:
Using Natural Language Generation to automate the production of commentary on profit and loss statements at a bank.
Goal:
Improved Employee Efficiency, Improve Operation Efficiency
Large Companies
Audio Signal Processing - Voice to text Conversion
For:
Lawyers and Judges
Scope:
Using Automatic Speech Recognition (ASR) to transcribe court case proceedings.
Goal:
Automate a Business Process
Precision Farming as a Service
For:
Farmers
Scope:
Use visual recognition to identify and help fight parasites attacking organic farms.
Goal:
Anticipate Risks, Improve Operation Efficiency
Autonomous Robot Improves Surgical Precision Using AI
For:
Hospitals using Autonomous robotic surgery via the STAR system
Goal:
Improve Operation Efficiency
Large Companies
Small Companies
Accounting and Finance - Automate Invoices and Expense Management
For:
Financial Controllers
Scope:
Using Image Processing and Optical Character Recognition to create operational efficiencies through automation of expense approval and reconciliation workflows.
Goal:
Improved Employee Efficiency
How ML Can Improve Churn Prediction to Retain More Revenue for Insurers
For:
Portfolio Managers
Customer Retention
Goal:
Improved OperationProblem addressed
The insurance company found that customers actively or passively churn due to various reasons. To note, active churn is when someone cancels their policy before its expiration date while passive churn exists when someone simply decides to not renew their policy. There was also a distinct lack of customer demographic data to develop a churn model.
Description
Tesseract Academy developed a churn technique using the Survival Model - an ML model which is more effective in solving and predicting churn. A Survival Model like this can directly model relative risk - that is the risk of one customer vs another customer. It also models this risk over time. The model included alternative data such as the device type, technical specs, when the customer joined, and their country of residence.
Outcome
Due to this solution, the insurance company was able to develop a good understanding of factors that contribute to churn. They now have a predictive model that predicts which customers are at higher risk of churn and when they are about to churn. On top of that, they are able to predict about 89% of the customers that churn with a precision of up to 90%, which means they can target about 4 out of 5 customers that will churn.
Raw Data
AI: Perceive
Machine Learning
AI: Understand
Automate Process
AI: Act