IBM 'Smart Solutions' Predict Fraud, Risk, Customer Behavior
IBM takes on high-profile problems with combinations of analytic products, pre-built models and dashboards, and supporting services.
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IBM announced Tuesday three IBM Smarter Analytics Signature Solutions that blend multiple analytic products, proven models and algorithms, and supporting services from IBM Global Business Services (GBS) to address fraud-detection, risk-analysis, and customer-insight scenarios.
The idea is to tackle high-profile business challenges where C-level executives and line-of-business customers want to move from being reactive to being predictive. "The emphasis is on outcomes, not products, speeds, and feeds," said Deepak Advani, IBM's VP of predictive analytics, in an interview with InformationWeek. "We're putting the pieces together to accelerate time to value."
The three solutions--Anti-Fraud, Waste & Abuse; CFO Performance Insight; and Next-Best Action--might include a range of products from IBM's vast software portfolio. An Anti-Fraud, Waste & Abuse solution, for example, might blend IBM SPSS predictive analytics, iLog rules management, WebSphere case management, and the IBM Netezza data warehousing capabilities. Use cases include claims-fraud detection at insurance companies and tax-fraud detection for government agencies. Healthcare fraud alone is a $250 billion-a-year problem, according to FBI stats.
CFO Performance Insight solutions are aimed at giving finance and other top-level executives predictive insight into business success and financial performance, with what-if scenario simulation and risk-analysis capabilities. Supporting products might include SPSS analytics, IBM Cognos performance-management apps, and TM1 in-memory analysis capabilities.
Next-Best Action solutions predict what customers might buy next or which customers might flee to a competitor. These sorts of analyses usually involve historical data from business transactions. But the latest wrinkle in customer insight comes from social network feeds, CRM comment fields, and other sources of unstructured data. Thus, IBM Cognos Consumer Insight sentiment-analysis software and IBM's Hadoop-based InfoSphere Big Insights platform are among the latest technologies that IBM can bring into a deployment.
All the solutions are predictive in nature, but exactly which components get used will depend on the use case and existing customer assets, Advani said. The other constant is supporting services, with GBS putting the pieces together and using best practices and intellectual property IBM says it has developed through more than 20,000 customer engagements. Example of intellectual property might include application-specific predictive models or pre-built dashboards and rules.
Of course, solutions are not simple, clear-cut products, so Advani said he couldn't really discuss expected costs or deployment timeframes (though you'd think they'd have a predictive model for that).
Projects typically start with a strategy workshop (to uncover goals and the as-is state of technology), he said, and that usually rolls into a proof-of-concept engagement that takes four to six weeks. The next step is to bring the proposed solution into production, with the goal being to see results "within weeks, not months or years," he said.
The seemingly open-ended Signature Solution approach might not be right for every customer, Advani acknowledged, but he said it appeals to buyers who are focused on outcomes.
"If somebody says, 'I know what I'm doing; just sell me a product,' we will do that, but line-of-business buyers are saying, 'we need help with the application of products to reach our goals,'" he said.
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