Financial technology, also known as ‘fintech’, refers to the application of artificial intelligence, blockchain, cloud computing, and big data in areas such as market support, payments and capital raising. Rapid innovation is taking place with fintech’s entrance into the financial services scene, forever altering the nature of commerce and end-user expectations in the financial services. With three of the four largest accounting firms’ pledges to invest over $9 billion in artificial intelligence and data analytics products over the next few years1, AI is sure to become an integral part of some of the most demanding and fast-paced industries, including the financial sector.
Over the past years, there has been a categorical shift to the widespread adoption of modern technologies in order to support day-to-day business practices among the largest financial accounting firms in the world. The use of AI in accounting and financial management for firms and businesses goes beyond strengthening accounting firms’ bottom line to also benefit investors, bankers and any other parties involved in building and growing businesses. Though large accounting firms have noted the tangible impact of AI’s introduction, several fintech start-ups are also looking to use these exact technologies to help start-ups and early-stage businesses make headway in the market.
Furthermore, with the use of AI, machine learning (ML) can take place. ML occurs as computers learn to optimize data based on relationships they discover without a more traditional, prescriptive algorithm. ML has the ability to generate new relationships between two variables that an individual may never think to test: Does the type of ice cream you like correlate to your likelihood of paying a loan back? Though these correlations may seem absurd, ML can identify trends in consumer behaviour and business operational patterns which can ultimately prove to be exceptionally useful to firms.
The Current Applications of AI in the Financial Sector
The rise of AI in the financial sector proves how rapidly it is able to alter the business landscape even in traditionally conservative areas. The examples below depict the most tangible impacts AI has had on the financial services.
AI and Personalised Banking
In the banking sector, AI powers the smart chatbots on firms’ websites which provide current and potential clients with real-time comprehensive help and solutions. This is incredibly helpful for firms, significantly diminishing call-centres’ workloads. Smart tech voice controlled virtual assistants, such as Google Echo and Amazon Alexa, are rising in popularity, especially due to the devices’ self-educating feature. This feature enables these devices to store the information they receive and process daily, making them ‘smarter’ and allowing tremendous improvements for the future.
In addition to smart devices, apps offering personalised financial advice are becoming increasingly prevalent on the app store, aiding individuals in the pursuit of achieving their own personalised financial goals. These intelligent systems have the ability to track a number of variables, such as income, expenses and spending habits, enabling them to curate optimised plans and personalised financial tips for individual customers. Even some of the largest banks in the US, such as Bank of America and JP Morgan Chase, have entered the AI space, launching mobile banking apps that provide their customers with reminders to plan expenses, make payments and interact with the bank in a more streamlined fashion, from completing transactions to acquiring information.
AI and Risk Management
AI has had perhaps the most astounding impact on financial services when it comes to risk management. The high processing power AI boasts allows high volumes of data to be processed in a short period of time, with cognitive computing managing both structured (spreadsheets and databases) and unstructured data (social media and the news), a task that would take humans faced with time constraints far too long. These AI generated algorithms complete analyses of all cases of risk in the past, allowing for the identification of potential future issues that the firm may come across in the future. Crest Financial2, a US leasing firm, first implemented AI in the analysis of risk without having to rely on traditional data methods, eventually enjoying an improvement in this area with a remarkable reduction in risk.
AI and Trading
Investments that are data-driven have steadily been rising over the last 5 years, having generated over 1 trillion USD in 2018 alone3. Investment - with the use of AI - is referred to as high-frequency, or quantitative, trading. These intelligent trading systems have the ability to monitor both structured and unstructured data in a fraction of the time it would take individuals to process it. Naturally, with faster processing times, faster decision making can take place, enabling faster transactions and ultimately generating more profit. Additionally, AI-generated stock predictions have been found to be significantly more accurate, as the algorithms have the ability to test trading systems through utilising past data and determining the algorithms with the highest success rates. Furthermore, AI can generate differing trading recommendations depending on an individual’s portfolio and the investor’s short- and long-term goals. Recently, Bloomberg launched Alpaca Forecast AI Prediction Matrix, a price-forecasting application that produces a combination of real-time market data with an advanced learning engine to identify patterns in price movements, allowing for highly accurate market predictions4.
Where can we expect to see AI head in the coming years?
Given AI’s remarkable impact on the financial services, one fact remains uncontested: AI will only continue to reshape the financial services landscape. With the expansion of cryptocurrency and blockchain, there are high hopes for increases in transactional and account security. Many believe that there will be an increase in data-sharing between FinTech firms and banks5, with successful methods of data generation allowing partnerships between firms developing financial models, and firms holding data, to flourish.
Evaluation: The Benefits
As mentioned above, the benefits brought by the imposition of AI in the financial sector are remarkable. However, despite AI’s most tangible impacts in the three aforementioned areas, there are a plethora of other ways in which AI can have small, yet immense, impacts. Firstly, AI works significantly faster than normal manual processes. In the financial sector, the phrase ‘time is money’ could not hold more truth. Consequently, with increased processing speeds and the reduced possibility of error, utilising AI is an obvious choice.
Additionally, AI can process very large volumes of information, essentially allowing for the curation of data-filled dashboards in real-time. In essence, computers will complete the manual work of data analysis and report creation. With the use of these insights and metrics, forecasting and budgeting is made immensely easy and efficient. Lastly, and arguably most importantly, AI has the ability to eliminate bias from any metrics; computer algorithms are not affected by emotions. Because of this, in areas such as portfolio management, rational decisions are made.
Evaluation: The Risks
Though the benefits that come with AI are overwhelming, there are also inherent risks that come with the use of AI in the financial sector. AI can effectively perform tasks which are traditionally set to be performed by humans significantly more quickly. However, the technology is not infallible, and instances where incorrect decisions have been made have occurred. Therefore, having the correct controls set in place, whilst ensuring that service providers are able to mitigate any errors that may arise, is of utmost importance.
Furthermore, with the use of AI comes the question of ethics. In the financial sector, AI is often used to solicit customers for new products and compliance purposes, and this is inevitably based on existing accumulated customer data held by banks. With this, the likelihood of banks breaching privacy laws in using data for these AI purposes only increases.
With humans being responsible for building AI-based algorithms, it is important that these individuals have a diverse range of characteristics and backgrounds. Given that firms rely heavily on the use of AI in prospective customer and employee screening, unconscious bias, and prejudices in the building of the algorithms that underpin AI may also have a significant effect on the employee recruitment process, as the decisions made by these algorithms ultimately reflect the flaws of their creators. As a result, it may be more challenging for firms to meet gender and diversity targets, possibly preventing them from building a broader customer base.
Conclusion
AI has the potential to lead to massive cost savings, particularly as financial services firms are well positioned to take proper advantage of its potential usages. Tremendous progress has been made in banking, trading, and risk management, largely as a result of the implementation of AI algorithms and features. However, this progress comes hand in hand with questions regarding ethicality and implicit biases. Trained and fuelled with data, AI has the potential to unlock new commercial and economic opportunities through introducing efficiencies into core financial systems. With a recent report from the FCA estimating that over 70% of firms in the financial services are now using some form of machine learning, it is evident that the uptake of AI technology is only set to continue increasing, allowing for further progress in the industry.
Sources
https://www.finextra.com/blogposting/20816/the-past-present-and-future-of-ai-in-financial-services
