Leveling up financial services through cognitive technologies

Smart or cognitive systems have reached new levels of sophistication. They can now learn, understand and interact in ways meaningful to humans. The financial industry can use these technologies to improve their operations and services, from customer support to investment advisory.

Smart is the new black

One tongue-in-cheek, yet insightful definition of AI is “everything a computer can’t do yet. As soon as it can, we call it obvious.”

Many complex or abstract capabilities have long been thought to be exclusive to the human mind. Yet, from playing Jeopardy to recognizing faces or driving cars, computers have relentlessly expanded the range of activities in which they can match or even outperform humans. This is made possible thanks to the increase in available computing power combined with advances in learning and pattern recognition algorithms.

Computers can now see, listen, read, and write, but also understand and give meaning. Skype can now translate conversations in real time. Facebook can recognize faces with better accuracy than humans (and the FBI). Natural language processing is used by many applications like Siri, Google Now or Cortana.[1] Sentiment analysis allows agents to determine the emotional state of their interlocutors and react accordingly. All these features contribute to make interactions with smart agents more and more easy, natural and pervasive.

But computers can now do more than interact with humans. Smart or cognitive technologies, in their various forms, are now shaping many businesses and industries.

Virtual assistants provide technical assistance to experts in aeronautics or oil-platforms to perform complex constructions and reparations. Embedded smart systems allow autonomous cars to self-drive millions of miles without accidents. Robots like Baxter can be easily be taught by, literally, showing them the actions to perform (no programming required!). And IBM’s cognitive system Watson has already contributed to cancer research by improving diagnosis accuracy as well as revealing new mechanisms of the disease. Watson even helps chefs explore new flavors and create new dishes!

Another notable development is IPsoft’s Amelia. Amelia can read and understand text, follow processes, solve problems and learn from experience. Notably, she understands implied, not just stated, meanings, and improves her performance by hearing humans deal with questions she can’t yet answer. Amelia can digest an oil-well centrifugal-pump manual in 30 seconds – and give instructions for repairs – and do the job of a call-center operator, a mortgage or insurance agent, even a medical assistant, with virtually no human help. You have to see it (her?) to believe it.

The more you learn

To achieve these feats, the underlying algorithms are not programed to cover every possible situation. Not only this is an impossible task (you cannot generate and store the translation of every possible sentence in every language), but this is also pointless in many situations (how can you write a guided algorithm if you don’t know the solution to the problem – e.g., curing cancer?).

So, instead of brute-force calculations, pre-programmed responses and keyword look-up, most of these algorithms learn to recognize patterns and infer rules and behaviors from examples.[2]

This learning component is key to make cognitive technologies work in real environments. Learning agents don’t need to be reprogrammed each time a new situation arises. They naturally improve and expand their capabilities with time.

Leveling up financial services

The financial services industry naturally lends itself to the use of cognitive technologies. The complexity of the financial markets, the vast amount of data, and the need for automation and better customer experience make cognitive technologies the right answer in a variety of situations.

In risk management and compliance, smart agents can evaluate all cases against approved policies and guidelines and understand the complexities of risk exposure.

Financial and market analysis can be made more insightful through the analysis of vast amount of information. In contrast to traditional analytics, smart agents can treat open-ended questions and detect key trends and variables that were not hard-coded in the system.

In wealth management, relationship managers advise their clients by analyzing large volumes of complex data such as research reports, product information, and customer profiles. Smart advisors can now provide cost-effective, personalized investment advice based on the ever-growing corpus of investment knowledge.

In fact, solutions like Watson and Amelia are already used by top financial institutions. For instance, DBS Bank uses Watson to identify the needs of wealth management customers, offer better advice and determine customers’ best financial options. Also, one of the biggest US banks uses Amelia to manage trading platforms and call centers, among several assignments.


We have now reached the tipping point where cognitive technology capabilities are powerful and reliable enough to be deployed in real life environments.

As all technologies before, cognitive systems will allow organizations to reduce cost, improve their processes, and focus on their core business. Financial services, like other industries, will benefit from these advances. With cognitive agents, banks and other financial institutions can readily improve their operations and services.

Cognitive technologies make it possible to automate knowledge work (i.e. tasks that require a certain level of knowledge or expertise) across a broad range of functions. Human presence and relationships will nonetheless remain essential, albeit to various degrees. Some activities will be mostly automated and only require some human supervision (e.g., call centers). Other activities like investment advice will remain conducted by human professionals, but will be enhanced by the use of cognitive technologies.

[1] These are surprisingly difficult problems. In computer science, it can be hard to explain the difference between the easy and the virtually impossible, as illustrated in the comic http://xkcd.com/1425/

[2] There is of course a wide spectrum of cognitive technologies (most of which were actually established decades ago), from machine learning to graph analysis and natural language processing. For instance, Watson is built around a data analytics engine and Deep Learning methods, while Amelia uses neural ontologies to give meaning and context to information. The various technologies and their derived products ultimately solve different problems.

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