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In a world of digitisation and self-service channels, the shrewd organisations are racing towards headcount and cost reduction to create competitive advantage – potentially signalling the end of the white-collar worker.
A small industry of consultancies armed with Robotic Process Automation (RPA)software are turning up at Bricks and Mortar businesses promising huge headcount reductions. In a flurry of buzzwords such as Artificial Intelligence (AI) and Machine Learning there is scope for confusion. I am often asked to demystify the relationship between RPA and AI and provide advice, for example: “Will RPA help reduce my cost of compliance?” or “Can AI replace my analysts and investigators?”
The hard truth is that key workers can, and will be, replaced by Artificial Intelligence, and it should be at the expense of RPA when reviewing the best options for your business.
To start, let’s lift the vail on some of the mystery around what these terms actually mean.
A formal definition for RPA
Fundamentally, RPA tools interact with user interfaces intended for humans to perform tasks against an organisation’s existing IT systems. Effectively, they are “scraping screens”, and then automating the entry of data based on some basic rules they follow, in the same way as a human would. So, they are robots, such as those that replicate workers on a production line, just without a physical presence. But generally speaking, they are not “intelligent workers”. They may use some basic AI or machine learning to help them mimic or reverse engineer the rules that the humans are following. However, they are not exhibiting AI in the same way that AI based decision systems may do. A good example of RPA would be customer KYC checks, where the robots can pull together multiple sources of data, access external systems to collect information and then interact with multiple internal systems to complete the KYC related forms and steps.
Intelligent worker replacement
Then there is the question of whether you can replace white-collar workers who are having to make more complex decisions; the more expensive resources that can often be bottlenecks to business objectives, if their expertise is scarce. The answer is yes, through the use of AI. However, don’t assume you can just throw a “deep learning super computer” at the problem, you have to capture the expertise of the scarce resource you are seeking to replicate and combine this with machine learning techniques.
Consider, for example, making a decision regarding a potential Anti Money Laundering (AML) alert, what would an expert user do? They would probably look at the business and the transactions they have made, and see if they are consistent with their historic behaviour.
For example, why would a fruit exporter in Latin America make a payment to a mining supplier in Africa with connections to the Middle East, and should we be concerned that a major shareholder has a directorship of a second company where the other director is a politically exposed person (PEP)?
This seems like complex analysis, but the right type of AI capability can make thousands of such decisions an hour. How? Join all the data sources together to get a view of the business, link up transactions and directors, use peer group analysis to understand what are normal relationships, use historic cases to identify high risk geographies and apply risk factors such as PEPs and sanctioned individuals. These are smart data sciences techniques, but this time informed by our very best AML investigators.
One could argue, and I would certainly be one of them, that through the augmentation of machine learning, coupled with the ability to join and analyse vast quantities of data, an “artificially intelligent robot” could replace high-end workers such as investigators, inspectors, auditors and underwriting credit decision professionals. However, this is a generation beyond traditional RPA, and is in the realm of AI.
How do the “Digitisation Professionals” do it?
As Governments seek to break down traditionally closed markets and introduce free competition for the better good of the consumers (PSD2 in banking being a good example), the markets are opening up to the “Digitisation Professionals”, such as the PayPals, Googles, Ubers, AirBnBs and Amazons of this world.
Unencumbered by old IT systems designed for paper based or phone interaction with customers, these “Digitisation Professionals” are embracing AI, combining it with big data, and using it wherever they can to pre-empt and remove the need for human intervention.
They are setting the bar for low cost of operation, customer acquisition and customer service. Essentially, they have bypassed the need for “sticky tape and plasters” approach of RPA and are embracing AI at the heart of their systems.
So, what does this mean for RPA?
As organisations enter the race to the bottom for headcount and cost removal, there is a limited window of opportunity to buy time and give yourself a head start by spotting and benefiting from RPA ROI opportunities.
After all, ripping out and replacing big IT systems can’t happen overnight, so as bizarre as it may seem, spending money now on putting in an IT system to talk to another IT system through a clunky UI interaction can make sense.
However, this must not be at the exclusion of striving as quickly as possible to the end goal of removing repetitive human based tasks by design. Furthermore, to win the race it will also be critical to introduce a programme of intelligent worker replacement in parallel.
So, take a critical look at your RPA programme: is the ROI case strong enough and could you skip straight to the intelligent worker AI solution, such as the AML alert investigation example? Finally, make sure you are making the most of your big data, representing it in the right way and applying the appropriate flavour of artificial intelligence treatment.
Imam Hoque – Quantexa COO and Global Head of Product