The Blue Polaris Blog

An update next on IBM’s solution for counter-fraud.  For IBM fraud involves an intentional act that misrepresents and is both illegal and designed for financial gain. They further divide this into organized and opportunistic fraudsters for whom different kind of detection is required. Fraud is growing problem because:

• Fraud schemes are increasingly sophisticated with organized crime moving it it due to both opportunity and lower risk of violence/arrest
• Fraud is no longer acceptable as a cost of doing business – regulatory environment is tigher for instance
• Consumers expect less fraud and are increasingly cranky about it, reporting it more readily for instance.

Schemes are getting sophisticated involved hacks, networks of people globally and laundering all being used together such as one that changed ATM limits on some compromised accounts, used people in many countries to withdraw and launder millions of dollars.

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Continuing the IBM Big Data and Analytics event we come to Watson. Watson is designed to understand natural language, human-style communication. Watson then trawls through a potentially very large amount of material to create and score some hypotheses as answers for these questions and returns them. With each interaction it also learns what works, what the better answer is. IBM has been commercializing Watson through their transformation plays where they work with large numbers of companies to try and solve big problems, by selling it to their enterprise customers and through the Watson Ecosystem where they have some thousands of use cases submitted and under development.

Watson’s ecosystem includes the Watson Developer Cloud, the Watson Content Store where IBM is adding new content sources to the pool and the Watson Talent Hub with subject matter experts. The Watson Content Store is being driven both by IBM and by requests from partners. The content is focused in di

Glenn finch and John Murphy talked about deepening business relevance. The challenge, he says, with much of the work around analytics and data is how easily it drops into discussions of security, integration, scalability, performance etc. What clients care about though is business relevance – business value – being created by the use of this plumbing. To make data, or analytics, relevant takes channels and strategy not just technology. This is why IBM has created its new Strategy and Analytics group.

This kind of business relevance is a journey – it takes time, multiple projects,new or changed processes and organizational change. Enterprises can be transformed one business area at a time as part of a journey to being an analytic enterprise. Where IBM has invested is in getting to these more analytical processes fast – 30 days to change a manual process to a more automated, analytical one. This is where the Watson Foundation is applied, using Watson te

Next up is a client panel with Verizon, FleetRisk Advisors and UBS AG.

• Verizon has created a specific Big Data and Analytics R&D group to diversify their portfolio of services, looking for new opportunities in data and analytics to leverage Verizon’s DAILY 12PB of data.
• FleetRisk Advisors is a business founded on predictive analytics for the trucking industry that rapidly focused on solutions to prevent the problems it was predicting – generating remediation plans.
• UBS’s e-discovery technology team is focused on delivering data in support of regulatory and other inquiries. In particular supporting (and understanding) unstructured data is critical.

As always I will try and capture the nuggets from the panel without attempting to transcribe it:

• Examples include personalized recommendations/advertising across many channels, intelligent network management, telematics-driven closed loop mitigation, detec