July 9, 2021
Evolving machine learning (ML) and artificial intelligence (AI) technologies are proving to radically disrupt a number of industries, including finance. ML is completely changing the game when it comes to fintech services and apps, namely for predictions and trend analysis, and overall customer experience.
According to Statista research, the use of AI in the fintech market in 2019 reached an estimated value of US$6.67 billion and is expected to reach US$22.6 billion by 2025, with a CAGR of 23.37%. The same report finds that many of the biggest names in tech, including Microsoft, IBM, Google and Samsung, are investing heavily into AI acquisitions and AI-related research and development.
Supporting this statistic, Accenture’s survey of C-suite executives’ adoption and plans for embedded fintech found that 84% don’t feel they will achieve their goals unless they scale AI. In addition, 75% believe they risk going out of business in five years if they don’t adopt the technology.
The survey finds that investment in AI and ML is predominantly driven by executives and leadership teams looking for ways in which to improve payment, lending and insurance processes, as well as improving customer experience.
ML and AI is enabling businesses to better understand and serve customers. Two main aspects of this is that ML solutions can dramatically improve response time and personalisation.
In today’s world, customers want information faster, and the faster businesses can respond the more likely they will earn the customer’s loyalty. Chatbots powered by ML can respond within seconds, and as the bot completes more interactions it will learn how to better respond to specific queries and provide more relevant and personalised information.
Along with chatbots, robotic process automation (RPA) is improving customer experience of fintech services. RPA utilises bots to handle repetitive or labour and time intensive tasks, without the need for human intervention. This reduces errors and allows employees to deal with bigger, more complex issues.
A growing subset of ML solutions is sentiment analysis. Sentiment analysis applications are designed to classify information as positive, negative or neutral. These applications can define certain words as positive, such as increase, growth and successful, or negative, such as fall and risk. This analysis can be used to develop personalisation for customers or make predictions about financial trends.
As ML and AI technologies continue to evolve, we are also seeing fintech apps specifically designed and built for the digital generation, integrating advanced technologies at every level. For example, more apps are being created to help users save, gain access to funds or invest as part of their everyday habits. These apps often cater to specific demographics, geographies or age brackets in order to adequately cater to the distinctive needs of each group.
One huge avenue for AI and ML in the fintech space is predictions and trend analysis. This includes robo advisors, algorithms for underwriting, and credit scoring and churn prediction. Real time decision making provides benefits for both employees and employers.
Robo advisors provide automated portfolio management and personalised product recommendations, with little to no human supervision. These advisors utilise information provided by the user and offer suggestions accordingly. Again, this frees up employees to tackle bigger projects or provide nuanced suggestions.
On underwriting, ML algorithms now have the ability to assess and predict the underlying trends that are impacting the broader finance industry to provide information about potential risk. Throughout 2020 and into 2021, we are seeing human underwriters being aided by automation, and as a result turning their focus to improving inquiry resolution, and developing dispute and fraud management.
ML and AI technology can predict the likely behaviour of customers, in order for banks and financial services companies to offer a more targeted service while still ensuring safety of the company. As an example, credit scoring software is able to analyse historical data based on previous lending operations, debts, marital status, financial behaviour and more, in order to understand whether an interested party will be a liability or a robust investment.
Also for banks and financial services companies, AI credit scoring can reduce non-performing loans and boost revenue, help to reduce risks, and speed up decision making processes. Being able to continuously analyse huge amounts of data, such as loan repayments, car accidents or company stocks, means that ML can enable companies to predict trends that will directly impact lending or insurance. Furthermore, they can be used to predict anomalies, reduce risk cases, monitor portfolios and provide recommendations on fraud.
Overall, ML and AI technologies offer an abundance of benefits to fintech services, including rapid and constant customer support, greater personalisation of services, more support for employees including freeing them up to focus on more complex tasks, the ability to predict risk and liability, and the ability to provide recommendations about investment and trends.
Moving forward, financial services companies will continue to invest in ML and AI technologies as they look to better serve customers, reduce operating expenses and increase profitability. We will see more partnerships and co-opetition as payments, lending and insurance firms work to retain and build their competitive edge.
As the technology evolves and use cases increase, we’ll see a growing need for conversations around data privacy and ethical practices. However, despite potential challenges, we’re already in the midst of an unstoppable rise in the adoption of ML and AI by organisations in the finance sector, as well as fintech born and bred companies making a name for themselves. Ultimately, the businesses that make smart investments and genuinely serve today’s customers will be those that win.
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