Aug 17, 2021

FinTech: AI and the future of financial services

Fintech
AI
Technology
ML
7 min
FinTech: AI and the future of financial services
AI has given birth to the fintech industry, making digital transactions and data aggregation a new way of life, but what does the future hold? 

Algorithms were the first form of technology in the financial services sector. In 1986, APEX (Applied Expert Systems) introduced PlanPower, a commercially applied AI financial technology that was used to create financial plans for those with an annual income over US$75,000 per year. 

Now, AI is a critical part of the fintech space in terms of collecting data, analysing information, safeguarding and facilitating transactions, creating customer-centric products, and streamlining processes.

Managing the AI monster

But with great technology comes great responsibility and the application of AI and data collection in financial services is one that raises many questions in terms of management, security and regulation. 

The European Union recently introduced rules that will begin to shape the way AI is used, with a particular focus on the financial services sector. Shawn Tan is chief executive of AI ecosystem builder Skymind, a machine intelligence startup company supporting the open-source deep learning framework Deeplearning4j and the JVM-based scientific computing library ND4J. 

He explains, "They [the new rules] include regulations around cases that are perceived as endangering people’s safety or fundamental rights, such as AI-enabled behaviour manipulation techniques. There are also prohibitions on how law enforcement can use biometric surveillance in public places with broad exemptions.”

Tan says some “high-risk” cases also face specific regulatory requirements before and after entering the market. Meanwhile, transparency requirements have been introduced for certain AI use cases, such as chatbots and deep fakes, where EU lawmakers believe risk can be mitigated if users are made aware that they are interacting with something that is not human. 

He adds, “These rules are important, and we welcome them - and we predict there will be other regions in the world that will follow the EU's lead because we need regulation to keep customers and their data from being exploited and to protect their civil liberties."

AI data and diversity

Diversity is a hot-button topic in terms of AI usage, along with the emergence of bias. Dr Garfield Benjamin, a postdoctoral researcher at Solent University in Southampton, UK, is working on social aspects of AI, algorithms, platforms, and privacy. 

Benjamin says the regulatory aspect of AI in the financial sector will likely include more thorough audits of training data and algorithms to identify areas where bias is treating people unfairly or blocking people from certain products. 

“We have seen that in advertising as well, where certain groups (often women or Black people) don’t see Facebook adverts for better financial products like mortgages or job opportunities.”

Benjamin continues, “The whole point of algorithmic decision-making is to discriminate – to judge people according to certain criteria like where they live, their age, their occupation. But we can design the algorithms and AI to support people or to make existing social biases worse. The future of regulation is looking to address some of these concerns.”

AI and cybercrime

As well as data regulation and bias management, the latest technology is also being used to improve security for financial service providers. Julian Dixon, CEO of Napier, a provider of anti-financial crime compliance solutions, says the incumbent anti-money laundering market was saturated with legacy technology providers that were falling short in their abilities to deliver to stakeholders. 

However, now the situation is improving, and the balance is being addressed, making users and their data safer, says Dixon. “The number of fines that organisations using this outdated technology were receiving was also on the rise, which suggested a clear problem with the processes that were in place.”

He believes there has been a change in thinking, and organisations are starting to embrace the use of machine learning and AI in compliance.

“You cannot underestimate the levels of criminal activity within the finance sector. Those who have prospered from illegal activity have an unlimited budget, an appetite to continue, and access to sophisticated technology.

“Compare that to your average AML (anti-money laundering) officer, and they are completely outgunned. The only way that you can begin to solve this is through the application of technology,” Dixon says.

Some experts also predict the permanent shift towards digital banking and contactless payments will lead to great levels of fraud - accelerating the trend towards using AI to track and identify malicious activity such as card payments and identity theft.

Jane Loginova, CEO and co-founder of Radar Payments, explains, "Artificial intelligence possesses the sophisticated power to replicate the analytical behaviour of human intelligence, as well as enable decision-making in real-time and offer predictive security notifications.”

She believes financial institutions and fintechs will continue to invest in AI-based security systems that “can significantly reduce digital attacks and spot suspicious activity in real-time.”

Loginova says the best systems will be integrated with artificial neural networks (ANN), which, combined with deep-learning models, will speed up data analysis and decision-making.

She adds, “It will also enable the network to nimbly adapt to new information it encounters in its system."

The customer experience

Ultimately though, AI has been most effectively employed as a vehicle for customer centric services. Aside from trouble-shooting chatbots, the technology provides an almost unending array of solutions to make life easier for the customer and incentivise the marketplace. 

Jenny Hotchin, Legal Practice Lead, iManage, helps law firms with financial services practices and in-house legal teams in organisations (including financial services firms) deliver legal services more effectively using technology, including AI. 

In her opinion, it is the handling of financial services through AI technology that has taken its usage to a new level. 

The advanced nature of AI today already greatly exceeds the average person’s data literacy level, Hotchin says, which is one of the reasons for increased regulation, including the proposed new AI Act. 

“I was one of those people that would only go and look once my card had been rejected for lack of funds. Even when I had online banking and a banking app in my hand, the experience was such that I checked it no more than once a week. 

“Now the user experience I get from challenger banks, products, and services is so rewarding I have totally changed my behaviour. I don’t just know my bank balance. I know what is going on with my mortgage, my savings, my investments, even my pension.”

Hotchin points out that consumers are so used to a seamless user experience that if it isn’t provided, they will simply seek it elsewhere. It’s become an expectation of the financial services space.  

“We get frustrated as the user experience we tolerate in our working lives is often far inferior to that which we enjoy in our consumer lives,” she says.  

Indeed, Hotchin argues that the increasing reliance on AI is not motivated by technological advancement but rather by human understanding of how best to apply it “usefully across all areas of the firm in a way that is rewarding and therefore adopted.”

The future of AI in financial services

As AI continues to be a growing force within fintech, experts believe its usage will spread across more sectors, increasing crossovers which will inevitably result in tensions - most specifically in the area of access to data. 

The pandemic has also caused an accelerated shift away from physical and towards digital communication, affecting the entire financial industry. 

But the motivation to increase AI within the sector will ultimately be driven by how much financial services organisations invest into upskilling their workforce. This upskilling is required to get real value from democratising insights, says Spencer Tuttle, SVP WW Sales at ThoughtSpot, the AI & search-driven analytics provider.

“According to the data, the industry is at a halfway point when it comes to upskilling their employees, with 49% of respondents saying training initiatives for employees to better understand AI are currently in place.” 

He adds, “An end goal is to be able to react at the speed of thought to changing conditions, markets, and information: Making the best use of time because getting to understanding has not been a fast process in the history of business intelligence.

The future of AI chatbots in financial services

According to Juniper Research, chatbots are the future of fintech customer servicing as they handle a multitude of requests from customers that can be managed by AI technology rather than human call handlers which can be deployed to deal with more complicated queries. Research shows that: 

  • Successful banking-related chatbot interactions will grow 3,1505% between 2019-2023. 
  • 826 million hours will be saved by banks through chatbot interactions in 2023. 
  • 79% of successful chatbot interactions will be through mobile banking apps in 2023.

Trends in financial services AI

Dan Johnson, Director of Automation, FutureWorkForce, says four main areas will see major changes within the next five years:

Process control and optimisation (PCO) utilising process mining and management tools will help companies make business processes more efficient, fast and increase overall productivity.

Customer Experience improvements utilising virtual or Robo assistant chatbots powered with AI and ML will respond within seconds. With the growing competition on the market, quick customer engagement will be a must.

Credit scoring: The majority of currently-used credit scoring systems are outdated. Their decisions are based on a supposed customer base, including demographics, age, marital status, possible preferences. AI and ML usage for decision making, compliance, and proactive customer marketing will be adopted to reduce churn and improve customer experience.

Insecurity, the increased use of AI by cyber defense tech companies will provide proactive mechanisms for fighting off attacks and protecting valuable data from hackers.

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