Machines to People
Artificial intelligence and machine learning are aiding in loss prevention and business processes in new ways.
- Pat Saporito
- October 2018
By Pat Saporito
The next wave of AI and machine learning can be used more proactively for loss prevention versus loss mitigation through a combination of digital and internet of things capabilities.
The digital age, or fourth industrial revolution that we are currently in, is being accelerated by several emerging and maturing technologies.
Perhaps none is so wide-ranging as artificial intelligence, with capabilities that enable systems to sense, reason, act and adapt.
These capabilities stem from cognitive and related technologies used in AI that can perform or augment tasks, help to better form decisions and create interactions that have traditionally required human intelligence.
Potential application of AI in insurance runs across the entire value chain, including sales and marketing, underwriting, claims, customer service and accounting.
While these capabilities offer great promise they also face significant challenges.
Technical hurdles include building complex models, providing enough data to train and feed the models and powerful database platforms to run the models.
But data and technology are not the greatest obstacles.
Vision, culture and change management are the largest challenges that companies may face.
The insurance industry has long embraced applications for underwriting, pricing and fraud detection. However, these applications are often siloed and use fewer single-threaded AI capabilities.
The next wave of AI and machine learning can be used more proactively for loss prevention versus loss mitigation through a combination of digital and internet of things capabilities, such as sensors or blockchain, combined with AI in a more holistic innovation strategy.
They also will be more feasible to integrate into existing business processes by building them as application programming interfaces and on cloud-based platforms.
Effectively combining these technologies will require five key factors:
• Identifying and prioritizing use cases in a broader risk management context.
• Reviewing needed and available data.
• Identifying tools and acquiring or partnering to address needed capabilities.
• Integrating models or applications into existing, redefined or expanded business processes.
• Building an AI culture and organization.
Engaging teams with representatives from across business areas as well as IT in proofs of concept, prototypes or pilots will help evolve the culture and change management.
AI-related skills and education programs will add confidence.
AI competencies should be added to professional development plans.
Employees should be reassured that AI will largely augment existing jobs, replace mundane tasks in those jobs and even create new jobs.
Acknowledgement and support of these innovation efforts by senior management will help the entire organization more easily make and accept change.
Best’s Review columnist Pat Saporito is senior director of SAP Global Center of Excellence for Analytics and author of Applied Insurance Analytics: A Framework for Driving More Value from Data Assets, Technologies and Tools. She can be reached at email@example.com.