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Cognitive computing use cases in BFSI

Shiv Kumar Bhasin, CTO, State Bank of India, on the benefits and uses of cognitive computing

Cognitive computing offers fundamental differences in how systems are built and interact with humans. Cognitive-based systems are able to build knowledge and learn, understand natural language, and reason and interact more naturally with human beings than traditional systems.

Interactive engagement with humans
These systems provide expert assistance by developing deep domain insights and presenting the information in a timely, natural and usable way. Cognitive systems play the role of an assistant, although one who is tireless can consume vast amounts of structured and unstructured information, can reconcile ambiguous and even self-contradictory data, and can learn.

Decision management
|These systems have decision-making capabilities. Decisions made by cognitive systems are evidence-based and continually evolve based on new information, outcomes and actions. Currently, cognitive computing systems perform more as advisors by suggesting a set of options to human users, who ultimately make the final decisions. To do so, the systems rely on confidence scores—a quantitative value that represents the merit of a decision after evaluating multiple options—to help users make the best possible choice, including why a particular recommendation was made.

Finding insights
These systems can discover insights that perhaps could not be discovered by even the most brilliant human beings. Discovery involves finding insights and connections and understanding the vast amounts of information available around the world. With ever-increasing volumes of data, there is a clear need for systems that help exploit information more effectively than humans could on their own.

Self-learning
Cognitive computing involves self-learning systems that are capable of interacting naturally with people to automate what either humans or machines could perform on their own. Cognitive computing processes are considered as next generation systems that continually acquire information using the data fed into them and process the complex information. These systems help in making better decisions by understanding the complexity of unstructured data.

Artificial intelligence, machine learning and big data
Cognitive computing systems use artificial intelligence (AI) and machine learning algorithms that enable them in anticipating new complex problems and model feasible solutions. Big data is increasing rapidly in volume due to massive digitalisation of business activity worldwide. Most of the information received is in the form of unstructured data, such as podcast, images, videos, symbols and natural language.

Intel, IBM, Qualcomm are developing chipsets that can mimic the behaviour of the human brain, and will be used for machine learning and cognitive computing to model human behaviour.

Prominent use cases:

  • Virtual assistant for
    • Wealth advisory,
    • Branch enquiries
    • Contact centre
    • Voice-based mobile banking
    • Chat-based assistant on portals
  • Digitising compliance operations
    • Fraud monitoring
    • AML monitoring
    • Sanctions checks
  • Google uses cognitive to improve the efficiency of data centres
  • Analysis of customer behaviour
  • Application testing
  • Digital infrastructure operations
    • Disaster recovery of the systems
  • Analysis and machine learning of security threats

 

Shiv Kumar Bhasin

Shiv Kumar Bhasin is CTO at State Bank of India 

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