Machine intelligence, machine learning, cognitive solutions dovetail with IA roadmaps and ethics
ADVANCED ANALYTICS & IA for Insurance MARCH 19-20Add bookmark
The Advanced Analytics & IA for Insurance agenda offers lessons learned for those at enterprises who are well on their intelligent automation journey way. It’s not just the analytics that are advanced here- this agenda is for advanced companies. Having said that, it seems that any forward thinking enterprise could possibly benefit from attendance.
Machine Intelligence busts the gate open on the Advanced Analytics & IA for insurance agenda with the promise of being a ‘cornerstone’ of the future of mobility. There’s not a ton of detail in the bullet points for the session, but the agenda doesn’t speak to machine learning…it speaks to a next step- machine intelligence.
Future of Mobility and Risk Management - Role of Distributed Ledger Technology (DLT) and Machine Intelligence
- Cornerstones of future of Mobility
- DLT-driven trusted machine intelligence in the next frontier of Mobility
- Amplified role of re/insurance in the shifting landscape of Mobility Risk Management
Jags Rao, Director – Swiss Re, Co-lead Distributed Ledger Technology, Swiss Re
After running to keep up with that first session, dive into the deep end with this next session which promises to discuss the benefits of automating the machine learning process. Most global corporate enterprises are just now starting to unpack how they can tiptoe their way in to the basics of machine learning technology. Manage your expectations by only hoping that the session delivers on solid lessons learned from implementing ML…but hope for the best.
Automating the Machine Learning Process to Improve Speed, Accuracy and Efficiency
- Organisational assessment to determine key business problems that need solving: Planning and prioritising key ML opportunities
- Understanding how automating ML can drive positive change across different business units and functions: CX, Claims, Pricing, Underwriting and Fraud
- From pilot projects to full scale deployment: Scaling projects at speed
- Case studies: Demonstrating value across the insurance sector
Neal Silbert, General Manager, Insurance, DataRobot
The next session is forward thinking– and dare we say advanced– because it simply brings up ethics as an issue for intelligent automation. This is a good first step in a universal conversation that should, at this point, be more developed. If for no other reason than it makes good business sense to be thinking what kind of ethics regulations could be implemented finding you scrambling to follow down the line.
Panel Discussion: Preparing for Intelligent Automation Era: Ethical Use of Technology within the Insurance Sector
- Where are we now, and where is the industry headed: Current and future technology
- What are the ethical and moral considerations of using intelligent automation technology?
- How can the industry ensure they are putting ethical principles into practice?
- The importance of transparency, accountability and responsibility
Hiek Van Der Scheer, Chief Analytics Officer, Aegon
Emma Kirby-Kidd, Process Automation Lead, Ageas
Kristina Grönvall, AI Strategist and Project Manager, AI Strategy & Acceleration, Nordea
Noting that intelligent automation is a journey is simply utilizing the parlance of our time. But if you’ve not yet conceived of the fact that you need a roadmap for that journey, stop reading and register for this event now. Really. Too many global corporate enterprises have short-term one-off plans for getting intelligent automation going. But this isn’t that. IA is a step in your enterprise digital transformation. If you’re not treating it that way, you’ll soon be in worse shape than you are now. And if you do have a roadmap, this session should prove to provide valuable pointers to bring back and include.
Implementing Intelligent Automation in Insurance: Developing and Executing an IA Roadmap
- Creating and setting a compelling vision and securing leadership support
- Piloting an IA project: Develop the business case, select use cases to pilot and ensure you have the right talent and skills in place
- Ensuring the right metrics are in place to measure success and re-engineer processes to optimise future projects
- Strategies for scaling up and expanding into other areas
Martin Malengier, Project Lead CoExcellence Robotics Process Automation, Belfius Insurance
DEVELOPING COGNITIVE SOLUTIONS
We do whiplash our way forward from the IA roadmap conversation to developing cognitive solutions, but this is a good representation of the landscape. Long-term thinking isn’t really discussed enough, but we need to balance that conversation with the next shiny object of cognitive solutions. But the tough news is that you need to implement cognitive solutions now to dive into your dark data to keep pace with your competition. So you kind of need to be a visionary pragmatist.
Intelligent Automation & Beyond: Developing Cognitive Solutions
- Extending and improving the range of actions from a typical RPA model and effectively transitioning to cognitive automation
- Advantages with cognitive automation: Cost savings, CX, accuracy and business process optimisation and scalability
- Leveraging cognitive solutions to managing the growing volumes of unstructured information
- Developing the strategy, developing pilot projects, measuring performance and optimisation
Sumeet Pathak, Director – Smart Automation, Societe Generale
Follow the link to see the full agenda and register for Advanced Analytics & IA for Insurance now.