Understanding AI in Finance | 2019

Artificial intelligence is becoming more and more prominent in many industries worldwide, but especially in financial services. This is because it boosts efficiency, lowers cost and increases accuracy and productivity as human labor can now focus on important things like strategizing and planning instead of mundane paperwork and similar tasks. Here’s all you need to know about AI in financial services.

AI in Financial Services

AI can be used in many aspects of financial services, such as credit decisions, risk management, fraud detection and prevention, trading, and personalised banking to name a few. Thanks to AI and machine learning, data can be processed efficiently and accurately in the form of customised algorithms in order to provide an improved service.

The figure above shows that companies are taking AI seriously. Image from deloitte.com

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For example, customers would previously have to follow the bank’s stringent processes and requirements for financial services such as loans, but today, AI has uncovered plenty of new ways to offer additional benefits and customised services. This would not have been possible previously due to prohibitive costs as well as the manpower required – in short, it would not have been worth it!

How is AI used in finance? 

AI and machine learning is perfect for financial services as it is an industry that is reliant and driven on numbers and data. There are three types of data that AI responds to very well – 

parameters and numbers, text and images.

A well-written algorithm will be able to quickly learn and recognise patterns to generate accurate insights beyond what humans are capable of. It will also be able to analyse, interpret and write text by utilising natural language processing that is context-aware, giving them near-human levels of accuracy. In terms of images, deep learning methods will ensure AI will spot patterns, understanding scenes and scenarios and recognise objects and faces. Some of the companies that are pushing the AI agenda can be seen in the infographic below.

Image from cbinsights.com

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A good example is JPMorgan Chase’s Contract Intelligence (COiN) platform – it utilises image recognition to analyse legal documents and pick out key data and clauses in a matter of seconds. If left to human labour, it would have taken a whopping 360,000 hours to manually review its 12,000 annual commercial credit agreements!

AI use cases/examples in financial services

At its core, AI and machine learning is designed to speed up processes and automate manual,repetitive tasks. This can include automation in banking whereby rules and regulations can be incorporated and a decision made in seconds. For example, approving a loan can be done automatically with AI after it has analysed all data available regarding a customer’s credit score. 

AI can also be used in reporting and analysis as it can quickly scan data available in accounts. This allows banks to quickly discover in real-time which accounts are performing and which are not, making it easier to single out underperformers and tweak it to get back on track. In the process, AI can also enrich transaction data due to it being able to recognise patterns and trends that humans cannot. The additional data deciphered gives financial institutions more context, allowing them to make better decisions faster.

Predictive analysis is another example of the power of AI and machine learning. As highlighted earlier, AI enables advanced pattern recognition which means it can be a powerful tool in personal finance for example. It will be able to recommend a suitable investment product, or whether a certain choice will be beneficial or not. This added information is key to obtaining and retaining customers, who feel empowered by this new information that will improve their financial health.

Understanding AI in Fintech

AI is helping FinTech companies grow at a tremendous rate because it allows them to provide existing services, but better, cheaper and faster than those offered by banks. It has also democratised financial services, putting power in the hands of the consumer and has allowed more people to access financial products that they may not have been qualified for under traditional banking requirements.

Understanding AI in banking

Not only has it simplified banking, the algorithms that make up a good AI have also created win-win situations for both consumers and financial institutions. All the data available is broken down into easy to understand conclusions, giving both parties all the knowledge they need to make better informed decisions.

Do banks use AI?

In a word, yes. Some good examples of use cases include chatbots, regulatory compliance, lowering costs and risk, and improving decision making. All these are made possible thanks to algorithms that utilise existing data to extract patterns and routes to highlight the best possible decision in any situation. It also highlights how important it is to learn and understand AI in finance, with many finance professionals acknowledging that they find it hard to keep up with the latest developments in AI and Fintech.

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Chatbots can extend the service hours offered by banks as it can mimic the mannerisms of a human being, making it a good example of how AI can improve an existing service at a relatively low cost.

Another example highlighted above is regulatory compliance, which is usually a mundane task that requires plenty of manpower to complete. AI can automate the process and quickly eliminate variables that do not meet certain criteria set by the algorithm, saving time, money and labour.

Examples of AI in Financial Services

AI and Credit Decisions

Artificial intelligence is widespread in banking apps as it can quickly assess a potential borrower accurately for a lower cost. It also takes more factors into account, resulting in a better, data-backed conclusion. Credit scores generated by AI analyse more complex rules compared to traditional credit scores, allowing senior leaders to differentiate between high-risk applicants and suitable candidates who may not have sufficient credit history.

AI and Risk Management

The sheer processing power of AI allows huge data sets to be processed rapidly, as well as going through both structured and unstructured data – something too tedious and time-consuming for humans. AI algorithms can also analyse historical risk cases and identify early warnings of troublesome cases, while also ensuring they remain up-to-date by learning and looking at new problem cases in real-time.

AI and Fraud Prevention

AI is becoming more and more successful at preventing credit card fraud thanks to machine learning which ensures that algorithms improve as time goes by. Fraud detection systems analyse a wide series of data such as client behaviour, habits and location, allowing it to trigger security mechanisms when a purchase does not fit within the established spending parameters.

Banks can also make use of AI to prevent money laundering as it costs less and is able to quickly identify suspicious patterns in cash flow, with aggregators like Plaid helping to reduce investigative workload of humans by up to 20%! 

AI and Trading

The usage of AI within trading has been increasing in popularity in recent years, spreading rapidly across global stock markets. Known as algorithmic, quantitative or high-frequency trading, AI can process data in a fraction of a second and automatically select which stocks to buy, giving traders a huge edge.

Systems monitor both structured data (historical charts, databases etc) and unstructured data (news, social media etc) before recommending a suitable portfolio for users depending on factors like risk appetite and goals. Some users may even trust the AI to manage their portfolios entirely, which is the case in tools like robo-advisors.

AI and Personalised Banking

Perhaps where AI truly shines is in the field of personalised banking. Many banks have chatbots and their own apps which not only extends customer service time, but also provides comprehensive solutions that solve user problems and reduces the amount of work for customer service representatives. 

Voice-controlled virtual assistants are also becoming more popular as the technology continues to improve. Thanks to self-learning, these assistants are constantly improving and can help users to perform a variety of tasks such as checking balances and account activity, scheduling payments, setting reminders and much more.

AI and Process Automation

AI is commonly used to automate procedures, and it is no different within financial services. Mundane, repetitive and time-consuming tasks can be completed in a matter of seconds thanks to AI and character recognition, saving thousands of work hours and labour costs. 

Software with AI can verify data and generate reports according to set parameters, allowing for easy document review, information extraction and more.

How can I learn about AI in Finance?

The emergence of various Fintech courses, both online and offline, serve to highlight both the growth of the various Fintech ecosystems around the world. However, there is currently a shortage of employees in the market with the relevant skill set. This lack of talent has created a gap in the market, and is an opportunity for new graduates or experienced finance professionals looking for a career boost. 

According to global recruiter Robert Walters, the number of Fintech jobs in the UK market increased by 61% in 2018, while the high demand also increased average salaries by up to 25%. Fields that saw an increase in demand for Fintech knowledge included compliance (85%), marketing (63%), sales (23%) and development and engineering (16%), highlighting how Fintech has has a huge impact on the job market as a whole.

The CFTE AI in Finance course offers a look at the impact of artificial intelligence on the finance industry. Senior figures and thought leaders in AI contribute modules to the course and you will gain an understanding of the various AI technologies and the challenges of merging them with existing services.

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