Mistakes are human, especially when manipulating a multitude of numbers on a daily basis. But what would happen if your bank made the same mistakes? Surely, it would immediately lose all credibility with its customers.

That's why so many financial companies are choosing automation solutions, and not just for processing numbers. Take the case of extracting data attributes related to stock trading announcements. This is the classic use case that Frank Chen, head of intelligent automation and the project management office (PMO) at J.P. Morgan, talked about during the Summit of our technology partner, UiPath, dedicated to artificial intelligence.

The case of J.P. Morgan

According to Frank Chen, automating this process has always been a major challenge because there is no industrial standards requiring a consistent document format. Therefore, the traditional tools of optical character recognition (OCR) cannot effectively extract the required data. Formats and content are constantly changing. In the past, if a business had a model to automate the process, any change or deviation from the model could end the automation and require a expensive and time-consuming repair.

The solution proposed to J.P. Morgan consisted of a three-pronged approach based on multiple products. The first step was to create a robot that visits the stock exchange's website and retrieves relevant action notices. Then, the notice of action had to go through UiPath Document Understanding to extract relevant attributes and fill a template with required information. This process was based on an AI-based data model. And according to Chen, it was this component that “changed the game.”

A non-technical user on the operations team created a model and trained it. Previously, the process required a long collaboration with a data scientist to create an exploitable model. Whenever changes were needed, the team had to call in the data specialist again and start the process again.

Finally, J.P. Morgan wanted formalize an examination process that would give users and developers confidence that the models they build worked as expected. And if something went wrong, they wanted to have the opportunity to retrain the model and continuously improve its results.

Our advice for banks starting out with automation

1. Start small

It's best to have a solid understanding of the capabilities of automation and to ensure that a tool works as it should before broadening your scope of action. To acculturate your organization, you will also need a successful proof of concept project.

2. Understand what you want to automate and why you want to do it

Automation is an effective solution in many cases. However, as with any decision, consider several options to find the one that best fits your situation.

3. Reuse, reassign, and evolve

The initial use case you create may have a wider application. Other teams and departments may face similar challenges. So, gradually increase the value of automation by exploiting and reusing existing automation components and involving other stakeholders in your project.

Factors driving the adoption of AI and automation in banking

First, the banking sector includes a strong documentary intensity, without global format standards. Banks process a large amount of semi-structured and unstructured data and the associated processes can be very time consuming. So automation and AI can take on an increasing part of this workload.

In addition, the industry has already automated many of its repetitive, rule-based tasks. Businesses are now looking ways to automate variable processes that blend rule-based tasks with smoother, unstructured data. This requires advanced machine learning, prediction, and other problem-solving abilities that reflect human cognitive skills.

Finally, all businesses now want to be more customer-focused. And to deliver tailored experiences, banks need to monitor and respond to unstructured data from a wide variety of channels, including email and social media.

About StoryShaper:

StoryShaper is an innovative start-up that supports its customers in defining their digital strategy and the development of automation solutions tailor-made.

Sources: StoryShaper, UiPath, J.P. Morgan