How is artificial intelligence and machine learning changing the financial services industry? Imperva’s Grainne McKeever explains

Artificial intelligence (AI) has become integrated into our everyday lives. It powers what we see in our social media newsfeeds, activates facial recognition (to unlock our smartphones), and even suggests music for us to listen to.

Machine learning, a subset of AI, is progressively integrating into our everyday and changing how we live and make decisions. 

Machine learning in finance

Business changes all the time, but advances in today’s technologies have accelerated the pace of change. Machine learning analyses historical data and behaviours to predict patterns and make decisions.

It has proved hugely successful in retail for its ability to tailor products and services to customers. Unsurprisingly, retail banking and machine learning are also a perfect combination.

Thanks to machine learning, functions such as fraud detection and credit scoring are now automated. Banks also leverage machine learning and predictive analytics to offer their customers a much more personalised user experience, recommend new products, and animate chatbots that help with routine transactions such as account checking and paying bills.

Machine learning is also disrupting the insurance sector. As more connected devices provide deeper insights into customer behaviours, insurers are able to set premiums and make payout decisions based on data.

Insurtech firms are shaking things up by harnessing new technologies to develop enhanced solutions for customers. The potential for change is huge and, according to McKinsey, “the [insurance] industry is on the verge of a seismic, tech-driven shift.”

Financial Trading

Few industries have as much historical and structured data than the financial services industry, making it the perfect playing field for machine learning technologies.

Investment banks were pioneers of AI technologies, using machine learning since as early as the 1980s. Nowadays, traders and fund managers rely on AI-driven market analysis to make investment decisions that are paving the way for fintech companies to develop new digital solutions for financial trading.

AI-driven solutions such as stock-ranking based on pattern matching and deep learning for formulating investment strategies are just some of the innovations available on the market today.     

Despite these technological advances, the concept of machine learning replacing human interaction for financial trading is not a done deal.

While Index and quantitative investing account for over half of all equity trading, recent poor performance has exposed weaknesses in the pattern matching model on which investing strategies are based and demonstrates that, no matter how fancy the math, computers are still no replacement for the human mind when it comes to capturing the nuances of financial markets. At least, not yet. 

Data Analytics for Security and Compliance

Managing enormous volumes of data make compliance and security two of the biggest challenges for financial organisations.

It is no longer enough to protect your network perimeter from attack, as the exponential growth of data and an increase in legitimate access to that data increases the likelihood of a breach on the inside. 

Additionally, banks are storing large volumes of data across hybrid and multi-cloud environments that provide even more opportunity for cybercriminals to get their hands on valuable assets. In short, the same data that brings new opportunities for business growth increases the security risk for financial firms.

Data analytics using machine learning has been transformational in helping firms overcome these challenges as it picks up on unusual user behaviour to detect suspicious activity and minimise the risk of fraud, money laundering, or a breach.

Similarly, data analytics technologies can be applied to compliance activities such as database auditing processes, reducing the need for human intervention and thereby easing the burden for compliance managers.

Looking Ahead

As the financial services industry continues to leverage machine learning and predictive analytics, the volume of data these firms generate and store is ballooning.

Protecting that data, other sensitive assets, and business operations will only become more challenging. Firms will have to adopt new security technologies that can mitigate their security and compliance risk.

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Nedbank Insurance, part of the Nedbank Group that employs over 31,000 people across South Africa, has utilised Machine Learning and Design Thinking to help speed up how it processes emails.

Challenge

Today, every business has an email address. And if you’re a financial services organisation with many business sections and over a million clients, that channel can get crowded.

Nedbank Insurance covers clients’ life and other insurance needs and receives thousands of emails about claims, policies, address changes, complaints, queries and potential new clients. Some emails are long; others are short. Many have attachments that must be categorised correctly to simplify processing for back-end teams. There are also unpredictable peaks when for example heavy storms cause inboxes to be bombarded.

Indranil Bandyopadhyay, Head of Business IT Enablement for Nedbank Insurance, suspected that an automated system could solve the problem and increase operational efficiency and client satisfaction.

“With the old system, a single person can, on average, process one email every 60 to 300 seconds (three minutes on average),” he said.

“That’s about 160 emails per dedicated resource a day. With email volumes growing, our backlogs were mounting, and we didn’t want this to influence our client service. We knew technology could help us create a more sustainable solution, but we needed the right partner.

“Nedbank Insurance had to try a couple of times to get this right. The challenge was that they [other companies] didn’t try to solve the real problem and looked towards their overseas counterparts to solve for this problem. Some of them went half-way and gave up.

“I also engaged with international vendors who mentioned they couldn’t solve the end to end problem. This made me feel we are really trying this for the first time in the world. I did not give up because I believed this could be solved.

“Grit and tenacity along with the right partner were the reasons we were successful with Synthesis.”

Solution

Following various methods to analyse email and attachment data, Synthesis built predictive Machine Learning (ML) models with natural language processing algorithms to understand the intent of the email. The models first learnt from the employees at a rapid pace, refining the process through feedback. Then, when the models performed reliably in terms of predicting where emails should go, they were routed automatically.

“I have been thinking about ML for the past three years,” said Bandyopadhyay.

“The benefits are huge. Especially in an admin orientated industry like Insurance.

“While RPA can deal with repetitive stuff, ML can be used where cognitive ability is needed to complete a task. The main challenge of ML and automation in general is the introduction of ‘positive feedback loop’. This has a leg for a process to go rogue and can have huge negative impact in a very short period of time.

“Sometimes, the amount of data to teach the ML algorithms is also huge challenge and the quality of data is also a major constraint.

“But what struck me is that Synthesis used Design Thinking to truly understand what the problem was. In a very short time they created a prototype, tested it with users and then adapted it. The solution they created revolved around genuine client satisfaction and not just completing a task, and that worked for us.

“Design Thinking assists in understanding the real problem and not the superficial issue. After asking 10 whys, one gets to the core of the problem.

“The issue for us was to find a solution to deal with a huge number of incoming emails. Our emails were in the Microsoft Inbox and the attachments needed downloaded on to a local PC.

“Once a human being understood what the emails were all about, they needed to go to our policy admin system and then categorised with the documents attached the documents.

“While we thought about using a ML/AI solution, Synthesis was quick to understand the real problem via DT. They quickly created a front end that pulled emails and attachments and, once the human being understood the content of the email, the front end automatically attached the document in the right category.

“This immediately gave 80% efficiency though no ML was used at this stage. This was a simple yet powerful solution that the users started using and got immediate benefit. This assisted in change management as well. At a later stage, we used an AI model to make sense of the emails and attachments and took the human element out of the value chain.”

Results

The new system has significantly reduced email processing time, on average from 720 seconds to 28 seconds.

“Clients’ experience will change immensely because now we can work on whatever our clients request immediately,” said Bandyopadhyay.

“During a peak, the machine can handle the volume of requests and now nothing hinders us from responding to client needs. This will greatly improve client experience and will support our commitment to serving clients.

“I am not surprised by how successful the solution has been because I slept, ate and drank this dream and was confident that this problem could be cracked. However, I was surprised to see the success rate from day one we went to production.

“Internally, there has been a very positive response. This solution is now showcased within the group and there are many interested parties that would like to use such a solution in this business.

In turn, Synthesis thinks Nedbank Insurance deserves recognition for its persistence.

“This is what it takes to pioneer advancements,” said Marais Neethling, Synthesis AI Evangelist.

“It will be great to support Nedbank Insurance as it continues its transformation journey and client satisfaction drive.

‘We have significant experience in using emerging technologies to help organisations transform, but we’ve found there’s something more important than technology – applying Design Thinking.

“We try to define an end-to-end system that is practical and includes all supporting processes.

“The challenge was interesting, and we had to work closely with stakeholders and create a plan with the Nedbank tech team.”

Nedbank Insurance now wants to leverage the solution across the business.

“Any electronic interaction is the next step,” said Bandyopadhyay.

‘All we need to do when someone calls in is to convert the voice into text – and route the call correctly. Artificial Intelligence and Machine Learning are the ideal ways for us to tap into the digital world truly to meet our clients’ needs. We are excited to delve further into the possibilities of this technology.”

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Evolution AI is creating machines that can read documents in any language so humans don’t have to and is taking some decision making out of their hands as well.

The company is one of the increasing number of startups that target the banking sector as a main customer base, but are not actually providing financial services.

There is a bit of a blurring of lines between fintechs, insurtechs and regtechs. Evolution AI has mainly financial services companies as customers, hence the fintech alignment but as its work often involves compliance for banks it also fits snuggly into the Regtech sector.

For example the company has done work for Royal Bank of Scotland (RBS) to automate its Know Your Customer (KYC) process. When potential customers sign up to RBS products and services all their documentation is read and cross checked automatically by Evolution AI software, which can then automatically make a decision on whether the customer qualifies. In the past people would read all documents and then make decisions.

Martin Goodson, who founded Evolution AI and is now CEO, was a machine learning scientist and had run data science teams at some London based startups. He them met Rafal Kwasny who had been working at investment banks in IT departments with a focus on big data and analytics.

It was 2015 when they kicked off the company. They had the tech expertise but having not worked in the business side of finance the two techies had to initially spend time talking to potential customers to help them understand the business needs.

Goodson said: “We needed to try and understand the marketplace and what where the very document based processes in banks. We knew the technology really well and wanted to understand how we could bring this to the enterprise world.”

The interest began much earlier. At an event in 2011 Goodson did a workshop in Deep Learning, before it was famous. “It was a small workshop and no one had really heard of it but it was really exciting. We saw what natural language processing, which teaches computers to read or understand human language, could do.”

So it wasn’t frustration in the workplace that triggered the founding of the startup but the realisation that a technology could revolutionise the way businesses work.

“We have an assumption that if we can teach computers to read documents and make decisions based on this there are a hell of a lot of business processes that could be automated,” added Goodson.

It was 2015 when Goodson and his co-founder built the first version of the platform combining their combined expertise in big data and machine learning. It could handle millions of documents, read them and make decisions based on that.

It got its first customer in 2016 when commercial data, analytics, and insights for businesses Dun and Bradstreet (D&B) signed up. In finance D&B provides services such as credit scoring and provides data to support KYC compliance. “It is an important part of the financial services ecosystem,” said Goodson. Dun and Bradstreet still uses the software.

KYC is the ideal home for Evolution AI’s software with its document heavy, labour intensive and repetitive nature.

In 2017 RBS Evolution AI’s first banking customer. The companies are working together on several projects, including RBS’s KYC project, but many have not been made public yet, said Goodson. “Other than UK government departments and Dun & Bradstreet all our customers are in the banking sector.”

For example Goodson said the company is working with another UK bank on reading invoices. “Most invoices contain similar information, but traditionally it has been difficult to automate the extraction of data because they come in many different varieties and formats. As a result businesses use humans to process them.”

For example Optical Character Recognition technology works well if the reader knows where to look on the page, but with invoices you can’t do that because the information could be anywhere.

“But our technology has a lot of cognitive flexibility and can understand overall document structure just by looking at the visual element on the page, much like a human does.”

Evolution AI has ten members of staff in London where its research and development is done, so most are engineers. I also has a small team in Poland.

The company received a large AI grant, about £800,000, from the UK government through Innovate UK.

“We demonstrated why our technology was more advanced than anything in the market and were successful,” explained Goodson. It has used the money to expand its capabilities to be able to read text in images. For example text in PDFs.

“It can read text in any format. Machine readable or not, we don’t care.”

How accurate is Evolution AI technology? Well the company has SLAs with customers today that mean accuracy has to be 99.5%. “The system knows when it doesn’t know and will pass to a human to check when this happens. It also learns from any corrections and improves.

The company is aiming to double its revenues every year. “We now need to get out there and communicate to the world what the technology can do.”

It recently launched fully self-service platform which allows customers to train the AI themseves.

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