Artificial intelligence has already made a significant, positive impact on the financial services ecosystem and we can only expect this trend to accelerate in years to come. AI has the potential to radically transform businesses but only if they deploy it with appropriate diligence and care. A 2020 report by EY and Invesco anticipates that AI will expand the workforce in fintech by 19% by 2030 as the industry stands to be one of the largest to benefit from the efficiency gains and innovation the technology can bring through operational optimisation, reduction of human biases and minimisation of errors in anomalous data. Alex Housley, CEO and founder of Seldon, further analyses the recent changes in the role of AI and the impact it is set to have on the finance sector in years to come.
Talent Shortage Within FS
According to a report by Bloomberg, listings for AI-based jobs within the financial sector increased by approximately 60% from 2018 to 2019. This demand for workers with AI expertise is not only seen within the financial industry but across a variety of other professional sectors, such as e-commerce, digital marketing and social media. The jobs market has had little time to respond, resulting in a shortage in access to talent. A study by SnapLogic found that whilst 93% of UK and US organisations are fully invested in the use of AI as a priority in their business, many lack access to the right technology, data, and most importantly, talent to carry these goals out. This ‘skills shortage’ is a major obstacle to the adoption of AI in business, with 51% of those surveyed acknowledging that they don’t have enough individuals trained in-house to make their strategies a reality. Machine learning can offer benefits in many forms and different businesses have varying needs. There is no ‘one size fits all approach’ when adopting and deploying AI, which can make it a costly process for many organisations not equipped with the right tools.
Fortunately, there is ample opportunity to enhance the responsibilities of numerous roles within their organisation or let employees get on with more strategic work. SEB, a large Swedish bank, uses a virtual assistant called Aida which is able to handle natural-language conversations and so can answer a trove of customer FAQs. This means customer service professionals have been redeployed to focus on complex requests and their more meaningful responsibilities. Even employees currently working within the industry are looking to broaden their skills to become more versatile across new technology-driven roles. In particular, financial services companies are looking to upskill their data scientists and analysts. They have the base skill set required and can do tremendously well with the right engineering support. Deploying artificial intelligence within a business’s infrastructure means it can take care of mindless, repetitive tasks and free up employees to focus on other, more rewarding parts of the business, maximising automation and cutting costs.
There is no ‘one size fits all approach’ when adopting and deploying AI, which can make it a costly process for many organisations not equipped with the right tools.
Enhancing Fraud Detection
One of the biggest use cases of artificial intelligence within financial services is fraud protection. With the rise of online banking and the exponential growth of digital payments, banks have to monitor huge swathes of transactions for fraudulent behaviour. This huge influx of data points poses major issues for the human brain but actually maximises the effectiveness of ML systems. We’ve seen significant growth in the use of deep learning, with most major retail banks now relying on machine learning tools to recognise and flag suspicious activity. To keep up with the pace of criminals and comply with stricter regulations, service providers have to look beyond traditional methods and implement hybrid strategies built around holistic understandings of behavioural and anomalous data.
Indeed, research by AI Opportunity Landscape found that approximately 26% of funding raised for AI startups within the financial services industry were for fraud or cybersecurity applications, dwarfing other use cases. This number is expected to rise as fraud detection and mitigation continues to be one the highest priorities for customer-facing organisations as consumers increasingly hand over their data in exchange for services.
Better Serving Customer Needs
Financial services companies are increasingly leveraging artificial intelligence to deliver tailored services and products for their client base. For those banks mining data effectively, AI provides the ability to serve customer needs across multiple channels, and in some cases to grow operations at an unprecedented scale. Tools such as chatbots, voice automation and facial recognition are just a few of the ways banks are using AI to streamline and personalise the user journey for their customers. Importantly, consumers are increasingly literate in automated services and their expectations are constantly rising as the technology improves, meaning organisations must constantly adapt or risk being left behind.
Chatbots and voice agents are also able to detect and predict changes in consumer behaviour, giving feedback on each interaction with a customer. All the results from customer touch-points are shared across the organisation, ensuring decisions and recommendations involving a human or machine are more intelligent and precise. Over time, these analytics mean businesses can make real-time decisions with their customers in mind, boosting engagement and personalisation.
In order to detect customer data from online purchases, web browsing and in-store interactions, banks must have AI in place to collect the data and automate decision-making. By adapting these technologies banks can connect their data, amplifying their offering effectively across all channels.
Continuous Adoption of Artificial Intelligence
Artificial intelligence and machine learning have already enhanced numerous capabilities for the financial sector, improving recommendations, customer experience, and efficiencies via automation. AI will continue to dominate different parts of the financial sector, and the acquisition of machine learning and data science talent will become the norm. A recent survey from the World Economic Forum attests to this, with nearly two-thirds of financial services leaders expecting to be mass adopters of AI in two years compared to just 16% today.
Acquiring the right talent to drive machine learning and AI in organisations will remain a challenge as innovation is focused in different areas and new technologies are being implemented. In lockstep with this will be the constantly evolving regulatory landscape surrounding adoption of AI in financial services as each side races to match and often contain the other. However, the multiple benefits that come from implementing AI and machine learning are clear, and it will be a key area of focus and growth for businesses within financial services over the next decade.