Artificial intelligence – key technologies for the financial industry

Author

Robert Koch

I write about AI, SEO, Tech, and Innovation. Led by curiosity, I stay ahead of AI advancements. I aim for clarity and understand the necessity of change, taking guidance from Shaw: 'Progress is impossible without change,' and living by Welch's words: 'Change before you have to'.

From robots on the factory floor to decision-making in investment banks, technology has always driven the financial service sectors. John McCarthy first coined the term artificial intelligence in 1956, but for many this concept from the world of science fiction is only becoming a reality today.

The potential of this technology has driven billions of dollars into research and development around the world; however, there are no clear examples or benchmarks that show us exactly where we may end up regarding making machines think like humans.

Artificial intelligence (AI) is a crucial tool in the financial sector. AI covers everything from chatbot assistants to new systems and tools designed to quickly detect fraud. In addition, AI tools can be used to improve task automation in the financial industry, helping to increase efficiency. While AI may provide a lot of obvious advantages, it’s important to recognize that even now, a significant amount of a bank’s manual procedures are still being done manually.

The benefits of automation and digitization aren’t being fully realized. One area where we should see some interesting progress in the future is trading – there are already systems that can trade autonomously, but this is just the tip of the iceberg.

Banks that employ AI can automate time-consuming procedures and significantly improve the client experience by providing financial advisory services at any time.

Understanding AI

Artificial Intelligence can be considered to fall into two different camps. Weak AI is the kind of AI that we have right available to us. This type of AI can accomplish certain tasks but can’t think for itself. Think about Google Assistant or Apple’s Siri as examples of this type of AI. While they may seem to be able to think for themselves, they’re merely acting based on algorithms.

Then there’s strong AI, which is the type of AI that can think for itself. This would produce a machine that could put together all the data it has available to it and make decisions based on this data.

Artificial Intelligence may be able to support banks in offering better services, but can it really help them do so? Understanding AI isn’t as hard as you’d think; the most difficult part is determining what type of opportunities and dangers come with it. This entails discovering methods to benefit from artificial intelligence while minimizing negative consequences of its use for businesses such as banks.

Many organizations have been investing in AI for a long time, and many more are now prepared to do so. The banking sector is experiencing massive changes because of new technologies that are continuously being introduced to the field. AI, on the other hand, isn’t designed to replace financial institutions; it exists to help them deliver better services for their clients. Banks have recognized that in order to stay competitive, they must embrace this technology.

Tip:

How well or poorly an AI system performs at the outset depends largely on the quality and quantity of data with which the algorithms have been trained.
High-quality data for training AI is available in all quantities at clickworker, according to your requirements.

AI Training Data

Applications of AI in Financial Services

AI is helping to transform all areas of finance and banking:

AI in Personal Finance

Customers typically prefer speaking with a human agent when they have a question about their account or an issue that requires troubleshooting advice – especially if they’re having difficulties understanding how an online platform works – but chatbots are helping banks provide quick responses for more routine queries.

Chatbots can access FAQs or databases to suggest solutions based on their responses, freeing up customer service representatives to field more complex requests.

Risk Assessment

We’re all familiar with the credit score as a means of determining eligibility to obtain a credit card. However, with AI banks aren’t limited to this simple metric when determining an individual’s creditworthiness.

Machine learning algorithms can be used to analyse hundreds of thousands of pieces of data collected across multiple channels – including something as simple as an individual browsing their bank’s website – which provides more accurate information than traditional credit scores.

Also, AI can be used to assess a client’s creditworthiness faster by analysing different variables such as social media profiles, mobile phone usage, or purchasing behaviour.

Banks are also deploying more intelligent chatbots to support branch staff with assessing risk for anti-money laundering purposes. By using natural language processing to understand what’s been said in a call, alongside historical customer data and sentiment analysis, patterns can be identified that may indicate high-risk behaviours or transactions.

This can then trigger a further investigation into factors such as unusual spending habits, the location of funds being transferred into or out of the country, and even any links with other flagged individuals.

Fraud Detection and Management

The task of ensuring that every transaction is legitimate falls primarily on the shoulders of fraud experts who must manually review suspicious activity reports (SARs) or alerts submitted by customers, employees, or financial institutions.

AI’s success in the financial services industry isn’t only predictable but also logical. Since AI learns from past data at its foundation, it’d be expected to flourish in the financial services sectors, where bookkeeping and records are second nature to businesses. AI-based technologies like machine learning are already becoming essential tools for fraud teams to detect credit card theft or identify other red flags.

Machine learning algorithms can be fed large amounts of data about past events to learn how specific transactions normally take place — anything outside these norms would then trigger an alert. Although human oversight will always be needed to investigate higher-risk cases, AI allows banks to detect fraud more quickly while reducing false positives.

AI in Consumer Finance

While high-turnover, retail-banking positions are being replaced by chatbots and smart assistants, investment banks have been using AI to automate commercial lending assessments for years.

One method of applying artificial intelligence to the banking industry is through a process known as Robotic Process Automation (RPA). RPA can perform simple tasks such as data entry or retrieving information from a website that previously required a person with an advanced degree in computer science.

Using sophisticated software that imitates human behaviour, RPA has successfully automated core banking functions like wire transfers and trade settlements while also streamlining more complex processes such as insurance claim assessments and new account acquisitions.

Trading

Understanding how the market is going to move is key for any financial institution.

It involves looking at thousands of data points and making decisions in fractions of a second. In today’s markets, this makes it nearly impossible without the use of artificial intelligence (AI).

In trading, AI acts as another tool that can help analysts make predictions by scanning through huge amounts of data from every possible angle.

Using AI allows traders to be more precise and knowledgeable and enables them to focus on risk management and what will happen next instead of wasting time trying to process all the information they have access to.

AI can also be taught to recognize patterns in previous data and forecast how they might repeat themselves in the future. While anomalies such as the 2008 economic collapse do exist, a machine may be trained to look at the data for ‘triggers’ that could cause these anomalies, and plan for them ahead of time.

Also, depending on each person’s risk tolerance, AI may recommend portfolio options to meet their specific requirements. This type of AI implementation has led to the growth of robo-advisors.

These are services that use algorithms to provide investment advice online, without a person involved. The service scans an investor’s market, checking their risk level and goals before making assumptions on what could be the best stock option for them based on their needs.

Machine Learning in Finance

The financial sector is just beginning to investigate artificial intelligence in its many forms. While machine learning is one type of AI that’s currently being studied, more sophisticated systems can comprehend natural language and speech in the same way a human would, allowing for more natural interactions with consumers.

This has the potential to be quite beneficial in allowing individuals access to services or information that was previously unachievable. One subfield, deep learning, has received a lot of attention in recent years because it is successfully applied in areas such as computer vision and natural language processing.

AI may have significant benefits for financial service providers by supplementing human abilities through greater pattern recognition and decision-making. AI systems have already been able to not only match but also exceed, the performance of humans in certain areas, making the use of AI a no-brainer.

How AI can Benefit the Financial Industry

AI’s potential in finance is vast, and the advantages of implementing it—particularly for tasks like automation and fraud detection—are enormous. By leveraging AI in financial use cases at both ends of the business process, companies can realize many different benefits, from improved customer experience to significant cost savings.

The younger generation consisting of millennials is fast becoming the banking sector’s most addressable client group around the world. This has forced financial institutions to increase their IT spending to keep up with higher digital criteria.

With digital banking channels being the most popular channel among these younger customers, many have claimed they’d rather avoid going to a branch at all, preferring to do all of their banking online.

This creates an opportunity for banks to reduce costs by replacing lower-value tasks with technology. As AI matures, it’ll only become easier for banks to automate human error out of middle-office tasks, increasing their clients’ trust in the institution’s capabilities.

Despite its early years, the progress of AI in the finance sector has been remarkable. It is only a matter of time before it transforms the finance industry completely—and what a difference that will make! Although it is still in its infancy, the rate at which it is evolving to improve the financial sector indicates that many positive changes are on the horizon.