Every day an infinite amount of unstructured data is being generated and stored by organizations across industries. To make sense of that data and apply it into effective decision-making, companies increasingly lean on AI-powered solutions. A particularly interesting area of AI – cognitive computing – is one of the forces driving transformation in the modern business environment. So how exactly does cognitive computing make a change?
What is cognitive computing?
First, let's define the term. As Dr. John E. Kelly III, SVP, IBM Research and Solutions Portfolio, puts it, “Cognitive computing refers to systems that learn at scale, reason with purpose and interact with humans naturally. Rather than being explicitly programmed, they learn and reason from their interactions with us and from their experiences with their environment.” Those systems generate reasoned arguments and recommendations about more complex – and meaningful – bodies of data, as IBM suggests in the report dedicated to computers, cognition and the future of knowing.
The most important part, however, is that “cognitive systems can make sense of the 80% of the world’s data that computer scientists call ‘unstructured’.”
Where can cognitive computing be applied in businesses?
In banking, cognitive systems can be applied to extract meaningful insights from major sources of unstructured data. Among those sources, professionals from IBM emphasize:
- Business scenarios involving a process or function that today takes humans an inordinate amount of time to seek answers and insights from various information sources to make a decision or think through a problem.
- Situations where there is a question and answer (Q&A) requirement that would require users to interact and ask questions posed in the natural language. This might include complex customer engagement scenarios that require deeper understanding and insights beyond question and answer responses provided by traditional programmable customer response systems.
- Processes or functions that require transparency and supporting evidence for confidence weighted responses to questions and queries.
Effective medical treatments can be developed in the healthcare industry with the application of cognitive systems to clinical data analysis. Moreover, cognitive systems can integrate implications of treatment recommendation for all parties – insurance policy, legal framework, etc. In law enforcement, cognitive systems can analyze data from previous investigation cases to recommend a course of action.
Cognitive computing in banking
In the attempt to secure a place in the ever-changing environment, financial institutions have to adopt modern technological advancements and redefine their relationships with the customer. Cognitive systems offer an edge in finding a place for a bank’s credit card in one’s wallet or app space on a smartphone’s home screen. Professionals emphasize the following ways in which cognitive systems can empower financial institutions:
- They offer personalized engagement between financial institutions and their customers by dealing in an individual fashion with each customer and focusing on their requirements depending on profile and relationship history analysis.
- Cognitive systems allow financial institutions to target people intelligently as awareness of customer personality enables intelligent targeting. Customers can be targeted with the right product, increasing the chances of sales and positively impacting both banks and customers.
- ANZ, which recently reported to be expanding its use of cognitive computing, sees its application in areas like advisory, risk, and back office automation. A particularly interesting area is the integration of internal and external systems. As ANZ CTO Patrick Maes commented, “...we wanted to build capability that was intelligent and that could deal with internal and external sources <...> where we could increase the automation and reduce time to market for approval of unsecured and personal loans.”
- Cognitive computing enables knowledge of an individual's personality to be used to match him/her to an agent most likely to close the business and drive value out of the relationship.
- Cognitive systems can rapidly digest trading and compliance policies, regulatory documents, and appropriate risk calculations and limits. It can then offer recommendations relating to the trade, all before market changes make those recommendations obsolete. Employing cognitive capabilities will enable teams to offer more opportune trade recommendations based on the latest information and market conditions, as IBM suggests.
- DBS Bank is one of the large financial institutions that has been relying on cognitive computing for some time now. Back in 2014, the bank turned to advanced analytics to guide highly customized investment ideas for customers. In particular, DBS Bank uses Watson to identify the needs of wealth management customers, offer better advice and determine customers’ best financial options.
- Since cognitive systems enable cost-effective and efficient measuring, monitoring and tracking compliance to financial regulations, they enable efficient identification, collection and use of customer information in seconds possible, i.e., the income tax that fixed deposits over a certain limit are subject to, as ET states.
- Cognitive capabilities can help the bank’s relationship managers analyze large volumes of complex unstructured and structured data, including research reports, product information and customer profiles; identify connections between customers’ needs and the growing corpus of investment knowledge, and weigh various financial options available to customers.
There are certainly more areas of banking business where cognitive computing can make a difference. While the process of cognitive computing automation may be a complex task for legacy-controlled ecosystems, it is a matter of survival for banks to pursue the implementation.