Deep Learning in Insurance: Driving Transformation to the Next-gen
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It is imperative that AI is going to be at the core for next generation apps. With almost all major tech players offering AI enabled solutions, we see it as a default feature. Although the Machine learning algorithms that power AI systems mimic the human brain for decision making, they fall short of the ability to explain the decision. While trusting these decisions usually required a leap of faith, recent years have seen an increase in regulations and oversight scrutiny, compelling organizations to find answers to the ‘Why’ behind AI decisions. This gave rise to a new field of research - Explainable AI ( or XAI), which focuses on understanding and interpreting predictions made by AI models.
This Whitepaper explores the current AI adoption in financial services, the ‘black box’ problem with AI and how explainability helps resolve the trade-off between accuracy, automation and being compliant
- Quick guide on overcoming hurdles of AI adoption - how organizations can build control, trust and maintain compliance with complex AI systems
- Opportunities for Financial Institutions to further leverage AI and Deep Learning systems
- How various stakeholders - Business owners, Data Scientists, Risk teams, etc. can reap the benefits of explainable AI
- Enabling swift, compliant and high-impact transformation to an autonomous AI system
Who is it for?
This Whitepaper on explainable AI is intended for Data Scientists, Business Leaders, and CxO’s. Further, end users consuming the AI decisions - Department Heads, Compliance, etc., may also find this Whitepaper useful.
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