Intelligent capture: A key piece to achieving a true digital transformation

digital transformation

Digital transformation is one of the most common phrases you’ll hear when speaking with financial services C-level executives about their ongoing foci. Technologies such as cloud, advanced analytics and artificial intelligence are all set to reshape the industry. The question is, how do you implement these technologies to enable your organisation to meet the demands of customers and regulators, whilst still working to bolster the bottom line.

With this change, financial institutions are on course to accumulate far greater levels of unstructured data than before. Applications for accounts and loans continue to require more verification. Meanwhile, multiple data points for customers multiplied by thousands of customers equals a huge amount of information – a combination of structured and unstructured data.

Capturing data – all of it

While the value of data and its relationship to success is well understood, many financial institutions are not capturing valuable unstructured data. The reason is straightforward: Legacy systems cannot support unstructured data without adding significant IT complexity.

To harvest this rich seam of insight, organisations must ensure that they can benefit from all available data. This means removing organisational silos, shared drives and shadow systems. Moving to a fully integrated platform is a top priority, integrating big data into everyday operations via more efficient processes.

The untapped seam of value held within this data is driving digitally aware C-level executives to transform their organisations to be more forward thinking. This thinking requires a balance of addressing current process issues, accompanied by accommodating the rapid shift towards digitisation.

The way to get there is by transitioning from small discovery use cases to managing new volumes of data for solving problems, unlocking inefficiencies and creating new value-added services. For example: Using intelligent search to uncover complaints, compliance risks and enhance the customer experience, and quickly processing petabytes of information to better mitigate risk, personalise offers and drive efficiency.

Managing input of the myriad sources of data is critical to a digital transformation. Seamless integration, API and web services all play a part in entering and parsing the data efficiently between enterprise solutions. However, some painstakingly slow and error-prone manual work is still required to gather much of it. This gap is often where robotic process automation (RPA) solutions can be of benefit.

We hear a lot of talk about robots and artificial intelligence (AI), but what is RPA, other than an industry buzzword? And is AI the same thing?

Let’s look further into this.

What is RPA?

Both RPA and AI are technologies within intelligent automation (IA). Deloitte defines IA as the combination of automation and artificial intelligence. If we take it down to its most basic position, IA takes a “doing” role, focusing on automating tasks. At its most complex, IA takes a “thinking” role, focusing on data-driven work that requires deduction and analysis.

Have you ever heard the term “swivel chair automation”? Robotic process automation is the deployment of software robots to significantly reduce the time, resources and errors associated with tasks that require workers to “swivel” in their chairs when changing focus between screens, systems and third-party information sources, like websites.

Financial institutions can apply RPA to many processes where employees do repetitive, relatively simple tasks that don’t require analysis to complete. While the discussion around workplace automation does include some concern about the elimination of jobs, many employees feel that removing these burdensome, error-prone manual tasks actually provides opportunities to expand their skills and take on new responsibilities.

In many cases, employees feel that automation creates positive change.

Businesses have a lot to consider when looking at adding RPA solutions. Besides reallocating workers to higher-value tasks, it’s also important to evaluate process efficiency on its most basic level – before adding software robots to the mix.

However, RPA will not magically resolve data quality issues or existing platform limitations. The old adage of “rubbish in, rubbish out” still applies in an IA world. If a process is fundamentally flawed or broken and converted from human-owned to robot-owned, the underlying data quality or connectivity issues that existed in the old world will also exist in the new.

What is AI?

So, RPA is on the “doing” end of the IA scale. Artificial intelligence, on the other hand, is all about teaching computers to think and analyse whilst performing tasks, like humans. Artificial intelligence technologies may include speech recognition, learning, planning and problem-solving capabilities.

The unstructured data explosion is happening. So what should you be doing now? Here are some great places to start:

  • Evaluate your current processes for potential improvements at a base level – before automating any tasks.
  • Investigate your silos, shadow systems and shared drives. What data are users storing, where and why?
  • Evaluate the process hours wasted by “swivel chair automation” and the cost to your business.
  • Investigate and plan implementation of an enterprise platform approach, seamlessly integrating with your core line-of-business systems and providing intelligent capture processes and electronic workflows.
  • Start researching IA solutions, including RPA and AI, and where these solutions could quickly make the most impact within your day-to-day operations.

To achieve a true digital transformation, you don’t have to transform your entire institution in one go. Begin with the end in mind by capturing the information you need to make more informed decisions, stay ahead of regulations and provide better service.

Colin Dean

Working from Hyland's London office, Colin brings more than 30 years of corporate experience ranging from enterprise content management (ECM) to natural language processing (NLP) for clients ranging from the Lloyds Banking Group to BUPA. Over this period, he has seen many changes in system and solution approaches, some successful and some that should not have seen the light of day. As someone who can remember when legacy systems were mere young kids on the block, you can guarantee he will have a point of view.

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