Fintech: Intelligent automation could add $512 billion to finance sector

Fintech: Intelligent automation could add $512 billion to finance sector

A new report from Capgemini’s Digital Transformation Institute reveals that, by 2020, the financial services industry could reap up to $512 billion in new global revenues through intelligent automation.

The report, Growth in the Machine, demonstrates the advantages of applying the right combination of robotic process automation (RPA), artificial intelligence (AI), and business process optimisation in the sector to achieve what Capgemini terms “intelligent automation”.

Capgemini surveyed 1,500 senior executives from 750 global organisations in retail and commercial banking, capital markets, and insurance. The survey covered organisations in the UK, US, France, Germany, Italy, the Netherlands, Spain, Sweden, and India, 42 percent of which had global revenues greater than $10 billion.

A business could already realise a 10-25 percent uptick in cost savings by applying RPA, according to Capgemini. However, that could potentially scale up to 30-50 percent with the application of AI-enhanced automation.

To date, automation technologies, such as RPA, have been implemented by the financial services industry to drive down costs and create new business efficiencies – mirroring how AI is being adopted by most industries, according to another Capgemini report this week.

Revenues or savings?

But leaders in the sector don’t see AI and automation in simple cost-savings terms, cautions the consultancy.

Leaders in the financial services industry have already begun taking automation directly to their customers, says Capgemini, adding that they are using it as a revenue generator rather than just a means to slash internal costs.

The report says that, on average, over one-third (35 percent) of financial services firms have seen a two to five percent increase in top-line growth from automation, with faster time to market and improved cross-selling being the key factors that influence gains.

Meanwhile, 64 percent of organisations from across different segments have seen improvements in customer satisfaction of more than 60 percent from the technology mix, according to the report.

Anirban Bose, head of Capgemini’s Financial Services Global Business Unit said, “The most visionary financial services firms have leaders with a sophisticated view of the potential impact that automation can have throughout their business. And they’re already reaping the rewards.

“Hundreds of billions of dollars in automation-generated revenue is up for grabs in the coming years. Only those companies that deploy this technology in a way that looks beyond cost-cutting and focuses on creating value for customers and shareholders will be able to win in the marketplace.”

Slow to adopt

With substantial gains within reach thanks to intelligent automation, it’s no surprise that an increasing number of financial services firms are considering deploying the technology on the front line.

However, despite the obvious opportunities, the adoption of intelligent automation has been slow to date. Only 10 percent of companies have implemented the technology at scale, says the report, with the majority struggling with business, technology, and staffing challenges.

The study finds that several factors are preventing organisations from moving beyond proof of concept to live deployment of intelligent automation systems.

For example, around four in 10 organisations (43 percent) are struggling to establish a clear business case. Many are also struggling to persuade leadership to commit to a cohesive intelligent automation strategy (41 percent.)

More, the successful deployment and scaling of automation programmes requires expert staff with a deep understanding of RPA and AI technologies. However, almost half of businesses (48 percent) say they struggle to find the right resources to implement intelligent automation effectively.

Meanwhile, 46 percent say that the lack of an adequate data management strategy is hampering progress, as AI-based automation algorithms require the right data to be available at sufficient volumes.

Capgemini reveals that only around one in four organisations has the technological maturity to implement cognitive automation technologies (comprising machine learning, computer vision, and biometrics). Most organisations still have traditional RPA, or – at best – natural language processing (NLP) in the backbone of their automation programmes.

Internet of Business says

Capgemini warns that exploring intelligent automation could be critical for the long-term health of the financial services sector, because of the growing threat from non-traditional players.

The study says that nearly half (45 percent) of organisations believe that so-called ‘BigTech’ players, such as Amazon and Alphabet/Google, will be their competitors in the next five years.

That much is certain. However, the report comes in the wake of two others this week: one from Capgemini on AI adoption in the enterprise, and another from Riot Research, suggesting that the AI bubble is set to burst.

Put the three reports together, and a granular picture emerges of AI adoption opportunities over the next five years, as a component of an overall trend towards automation.

The conclusions are straightforward. First, organisations that rush to implement AI purely to cut costs are adopting the technologies for the wrong reasons, as a short-term tactical move rather than a long-term strategy of business enhancement.

Second, enterprises should use AI as a means to get closer to their customers – with their active support – and not as way to keep them at arm’s length in a battle for data ownership.

Most customers welcome AI was the surprising finding of the Riot Research report; robots and AI that has a human face are noticeably less popular.

And third, the hype cycle in the current wave of AI adoption is coming to an end, and market consolidation and realism will form the ‘trough’ that follows the peak.

So intelligent automation should be as much about the strategic application of human intelligence as an arms race in clever technologies.

Chris Middleton
Chris Middleton is former editor of Internet of Business, and now a key contributor to the title. He specialises in robotics, AI, the IoT, blockchain, and technology strategy. He is also former editor of Computing, Computer Business Review, and Professional Outsourcing, among others, and is a contributing editor to Diginomica, Computing, and Hack & Craft News. Over the years, he has also written for Computer Weekly, The Guardian, The Times, PC World, I-CIO, V3, The Inquirer, and Blockchain News, among many others. He is an acknowledged robotics expert who has appeared on BBC TV and radio, ITN, and Talk Radio, and is probably the only tech journalist in the UK to own a number of humanoid robots, which he hires out to events, exhibitions, universities, and schools. Chris has also chaired conferences on robotics, AI, IoT investment, digital marketing, blockchain, and space technologies, and has spoken at numerous other events.
  • “organisations that rush to implement AI purely to cut costs are adopting the technologies for the wrong reasons, as a short-term tactical move rather than a long-term strategy of business enhancement.” – Indeed, it’s necessary to surmount an over simplified understanding of the benefits of RPA, and understand those beyond cost reduction. Consider improved compliance, quality, and productivity, etc. Scaling to enterprise level increases chances to observe the bigger picture when evaluating the benefits. (In fact, we at CiGen have adopted equating the scaling stage with a maturity level of RPA deployment.)

    “around four in 10 organisations (43 percent) are struggling to establish a clear business case. Many are also struggling to persuade leadership to commit to a cohesive intelligent automation strategy (41 percent.)” – Scaling requires a certain degree of foresight, operationalised in terms of medium- and long-term strategic planning. All along the way, potential difficulties must be acknowledged and addressed, if possible before they become actual obstacles. Think employees’ resistance, keeping expectations realistic, deciding upon the right sequence of processes to be automated, figuring out the targeted scope of automation, etc.