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The risk models of Agentic AI are still being set up. You're just getting influenced by hype cycle of Agentic AI or Agent driven AI enablement. The costs and degrees of hallucination are extremely high at of today and token optimisation is still in its infancy. I agree you are highlighting the potential, but Insurance, Banking are very heavily regulated sectors and mishaps going to get very costly. 

Good read 

A good read. Thanks for sharing.

Why I need the limitations of DWH when I can put through an OLAP solution in the cloud to sort with issues my data and build my compute with spark or java to manage the parallel processing to cater scalability challenges. 

If the intention is not to disrupt or minimally disrupt the codebase by lifting and shifting the DWH from on-prem to the Cloud and continue with the legacy codebase then you're not solving the problem but just deflecting it for a later day.

I would advise companies not to fall in this trap and break away from the legacy mentality and break down your code to actually adopt API & microservices approach where ever possible. Start small but create a rolling down effect.

Why Cloud-based Datawarehouse. The whole movement is to adopt streaming datasets or even no-sql data sets approach on cloud. Putting data warehouse on Cloud means putting heavy ambassador body on a powerful V10 engine which works best with a carbon fiber body to perform at its best speed. The approach you mentioned doesn't make sense from real-time surveillance performance perspective.

Ohh yes it can, and it is. But, that is taking its own evolution curve. Auto data fixing is another stream where AI / ML are taking giant strides and more tools are coming up. Read through my other blog post on RPA from Nov-2018, that may help.

I believe the change and the realization of no two ways about it is coming to the financial sector. At least that's the rising theme in Singapore and all companies are trying to push the envelope of innovation further. Would be interesting to see how other geographies coping with it

Very valid perspective and I couldn't agree more. Though I feel an independent view on the ERM already exists in Financial institutions like banks or Insurance companies in the form of Internal Audit teams.

In case there is more to be done then these internal audit teams can easily scale up to meet such criteria. 

I think an ecosystem that validates and supply Fraud surveillance can go very far in this direction. Banks already perform fraud assessments and they can provide retailers Fraud Risk resolution services as an added product.

Your example 4 doesn't need a Chief Experience Officer but a product owner who knows what he is doing. Of course, IT team will do as they're directed, mapping a new feature to right segment is a functional requirement, who did that UAT. Why this basic thing was missed in UAT. feature segmentation is the primary thing in such kind of feature development. putting a Chief Experience Officer is nothing but a job creation for another guy who is feeling an identity crisis.