Artificial Intelligence is the next big challenge in software development for business. Over the next 10 years, Artificial Intelligence will be a key component of success across public and private sector institutions.
“I skate to where the puck is going to be, not where it has been.” – Wayne Gretsky
From business to education to the military, the ability for machines to “learn” creates a significant opportunity to amplify the contributions made by a group of people. Machine learning, a type of Artificial Intelligence, lets small and mid-size organizations compete effectively against larger companies despite their smaller workforce and inherent cost structure limitations.
How does it work? Machine learning occurs when a program is taught to interpret data collected through automated and non-automated processes. By performing real-time analysis on this decision and updating its decision-making algorithms accordingly, the system “makes a better decision next time”.
While leveraging historic and third-party data analysis is always helpful at the outset, over time the software identifies its own predictive factors and constantly “self-improves”. Because of this, machine learning software cost-effectively helps companies make better decisions. It also greatly improves customer experience by offering customized customer journeys that consider individual consumer preferences.
Artificial Intelligence Case Study – Elagy
Elagy is an InsurTech startup that is disrupting the insurance industry by integrating real-time analytics and actionable insights throughout their distribution, underwriting and policy administration process.
- Insurance clients include a variety of companies within the insurance industry, including Swiss Re, AIG, Plymouth Rock, Accuquote, etc.
- Elagy’s strong analytics team uncovered many predictive attributes across a number of insurance product lines that drive performance in the distribution, underwriting and policy administration processes
- The company was founded by former insurance executives and industry consultants with a track record of identifying insurance company profit opportunities
Elagy needed a partner to develop a comprehensive technology platform that integrated its analytics services alongside insurance companies throughout their business processes. At its core, the platform would help insurance companies increase sales while improving their combined ratios.
About the Challenge
- Big data sets from multiple disparate data sources, including legacy systems
- Integration with distribution partners, 3rd party data sets and customer proprietary databases
- HIPAA, PII, PCI and TCPA requirements
- Despite being a startup company, Elagy needed to immediately provide scalable resources to large multinational organizations
The Elagy Data Analytics and Decision Making Engine provides robust data aggregation, data analysis, and machine learning capabilities that deliver real-time, actionable marketing insights to insurance companies. From autonomously bidding on marketing leads to prioritizing leads in a call center dialer, the Platform integrates with an insurance organization at multiple points in its business processes.
While operating, the Elagy Platform learns from insurance company lead and policy performance data. It can train on any point-in-time historical snapshot in just about any format. The trained model lets organizations more effectively pursue sales and policy retention/administration opportunities.
In addition, the program’s artificial intelligence generates real-time improvement of the system with or without human intervention. It uncovers non-intuitive attributes and has identified valuable profit predictors in the insurance sales and policy management functions. These discoveries let Elagy customers “buy better and manage better”.
One important highlight of the platform is that it provides easy integration with existing IT infrastructure, including legacy systems, through a secure API. Given that it’s commonplace for organizations in the insurance industry to have legacy software and database infrastructure, this is a clear value-add in terms of cost-of-adoption and ROI for Elagy’s customers and partners.
Given that Elagy is a Startup, getting a product to market quickly was key to their success. Jaroop met with Elagy leadership and also interviewed the entire Elagy staff. Working together, the Elagy analytics team and Jaroop put together a plan to build the initial analytics and decision-making engine.
Software Development Project Results
- Initial prototype was completed within 30 days, including integration with third party data and customer proprietary data
- Fully independent artificial intelligence, no human oversight needed for day-to-day administration or learning model
- Full-access API capable of collecting all data from multiple legacy systems, intelligently interpreting the data and returning a response to assist with decision making and prioritization
- Real-time, dynamic bidding on insurance leads through a ping/post process using statistically relevant predictive factors to optimize purchasing
- HIPAA, PCI and PII Compliant and Scalable Software
- Throughout the relationship, a reported bug rate of less than 0.3%
Client Success Results
- 22% improvement in premium generated per marketing dollar spent
- Quantifiable backlog of moderately valuable leads purchased during overnight bidding at less than 5% of daytime bidding costs
- New client opportunities due to technology innovation
- Reliability of 99.997% uptime since launch
- 76% decrease in labor costs associated with obtaining data to make decisions