Recently the Financial Conduct Authority (FCA) explored the use of synthetic data in financial services. The plan, launched in March, focused on incumbents and startup companies and explored industry views on the potential for synthetic data to boost innovation in finance and the possible risks and limitations. Synthetic data refers to artificial data created via algorithms. One of the most infamous types of synthetic data is ‘deep fakes’, which produce artificial information. The technology is generated by studying patterns and the statistical properties of data and with algorithms creating these patterns within a synthetic dataset, replicating real-world information. The main advantage of this format, compared to real-world data, is that synthetic data utilises information without identifying specific people. As long as no person can be identified within the synthetic data, data-protection measures do not apply.

As companies focus more on data business strategies, the opportunities to use data analytics to generate more valuable insights based on business and customer data continue to rise. However, as more data is integrated within a company, the risk associated with data privacy controls required to manage personal information increases. In the finance industry, the bulk of customer data is considered very sensitive. This is where synthetic data can provide an opportunity for finance businesses. Synthetic data is a privacy-controlled system that fabricates information in a way that replicates various trends within ‘real’ data sets. The synthetic data can replace other real data sets to support insights gathered from synthesised data, protecting privacy rights that could be compromised within a real data set.

With many data analysis techniques, there is a potential risk that information can be connected to a person, but synthetic data does not carry this risk. In the finance industry, synthetic data is used as test data for new products, for model validation and AI training. The FCA has emphasised that many challenges of today’s AI industry are related to a lack of data, datasets being too small, or a lack of access without potentially breaching privacy rights. In a recent consultation, the FCA explained that historical data can often be biased and unrepresentative, and algorithms based on this information will replicate these biases. Synthetic data could provide a solution to these problems.

Aside from eliminating data privacy concerns, the technology can fill in specific gaps where data required is low or doesn’t exist. Synthetic information can be used to create realistic but uncommon scenarios, such as risk management within financial services.
Synthetic data could offer a solution to the challenges between emerging technologies and the barriers concerning what production data can be leveraged. Many financial businesses operate expensive processes to control the risk of privacy and data protection breaches.
When applied correctly, synthetic data for analytics eliminates the overall risk of a breach. Synthetic data represents a major mitigating factor in managing privacy risk. Detached from operational overheads, the marginal costs of analytics are reduced considerably, enabling companies to scale their analytical goals and accelerate innovation.

Synthetic data could enable further access to data across the finance industry by widening access to data assets with incumbents and new businesses. As reported by the FCA, data access on an individual basis is possible through consent processes, but developing new technologies requires broader access to large data sets.

A key barrier impacting the adoption of synthetic data relates to trust – questioning whether the data represents an accurate representation for generating valuable insights. There is an opportunity here for regulators to support and promote the integration of synthetic data through a transparent standardised framework. The FCA has shown an interest in possibly taking responsibility for being a synthetic data regulator to manage the potential challenges. Implementing an FCA-approved standard would enable businesses to take their data and create a synthetic dataset to apply to their projects. This approach would drive greater adoption of synthetic data, increasing trust in this information being representative, and regarding compliance, the risk is managed by ensuring synthetic data meets regulator-defined criteria.
Further collaboration with other regulators will also be critical to creating additional standards for producing synthetic data from a business’s information. Without this, wide-scale adoption would struggle as the investment to deliver specific synthetic datasets would require significant funding.

Some industry experts consider data as the new oil. Just as it does for the finance industry, the rapid digitalisation of the economy comes with opportunities and challenges for financial regulators. On the positive side, new information is available, with vital insights into financial risks that regulators spend considerable time trying to understand. The abundance of data provides details on global money patterns, economic trends, onboarding decisions, noncompliance with regulations and many more critical subjects. More importantly, the data provides answers to regulators’ questions about the challenges of new technology. 

Thanks to digitalisation, regulators have the opportunity to collect and examine much more data and see more of it in real-time. The possibility for issues develops from the concern that regulators existing technology cannot harness the data. Ironically, this rise of new data is overwhelming for many companies. Without applying digital technology, the stream of new data financial regulators need to manage systems cannot be used appropriately. This challenge of managing the abundance of new data is where artificial intelligence can play an important role.

In 2019, Mark Carney, the Gov of the Bank of England, emphasised that financial regulators needed to integrate AI to maintain pace with the rising amount of data flowing into businesses. Carney highlighted that the bank received 65 billion pieces of data every year from companies it is responsible for, and examining all of this information would be overwhelming without supportive technology. In today’s world, the volume of data has only continued to increase, especially if you factor in other data sources generated from public records, news and social media channels.

AI emerged over 70 years ago, and for years AI experts predicted that it would change our lives significantly, but it has taken a long time before we have seen the impact of AI on our daily lives. It was only until recently that we discovered the signs of AI and how it could solve real-world problems. This discovery is down to having enough data available in a digitised format to justify using AI. Today, we have so much data available we can use AI, but in sectors such as finance, AI is becoming necessary to maintain pace. Financial regulators are beginning to explore how AI and similar technologies can improve their work. Businesses continue to test the potential of new technologies to monitor performance. This work is happening in the finance industry, particularly to enhance compliance systems.

Financial regulators worldwide have become more active in monitoring the use of AI rather than adopting it for their benefit. How can AI be used to improve areas of poor regulatory performance? One example has emerged from the war in Ukraine. The Russian invasion has triggered a new level of sanctions against Russian oligarchs attempting to hide their money. Financial institutions are obliged to monitor accounts and identify transactions by these sanctioned groups. If law enforcement agencies had applied AI-powered analytics to examine data from global transactions, they would be able to detect particular patterns within sanctioned groups. For the time being, however, most financial groups lack these resources. 

Another example relates to the millions of refugees and the issue of human trafficking. Banks are required to maintain anti-money laundering systems to detect and report the movement of money that could indicate human trafficking and other crimes, but many of these systems fail to be very effective. 

AI-powered compliance systems would be far more efficient at detecting these issues and significantly impact many challenges our planet faces.

AI can leverage a bank’s biggest asset: its data. This can provide traditional finance businesses with a new source of potential income.

It’s clear today and technology matters in the finance industry. The new emerging fintech displays the power of integrating technology with finance. It’s understood that some of the leading businesses such as Monzo and Revolut have succeeded in securing large numbers of customers predominantly because they were one of the first to automate the process of creating a bank account, replacing the traditional time-consuming way of setting up an account.

An automated process like this involves managing data, and as this becomes more advanced, it is often referred to as artificial intelligence (AI). Chatbots represent one of the most common forms of visible AI in finance. WeBank of China reports that nearly 98% of all customer enquiries can be managed via chatbots. Aside from the overall customer experience, AI can enhance finance systems, reduce costs and improve overall margins.

Data represents the biggest factor for conventional businesses to com[pete against fintech. Incumbents are gradually transforming in terms of data and digital technology. Their size and availability of resources provide traditional finance with a significant advantage over fintech and can allow them to catch up relatively fast.

Traditional finance businesses are investing rapidly in AI solutions, with banking scoring the highest of any industry for adopting AI, based on a recent study by GlobalData. The data incumbent finance businesses have gathered through their long years of building a customer base enables a relatively quick closing of the gap if applied with an AI strategy. Once this gap with fintech is closed, the new businesses may not have as clear a competitive edge as before. The Financial Times recently stated that the current performance of fintech banks during the pandemic suggests the concept that leading fintech companies can do anything conventional businesses can do better is diminishing. While fintech has had the initial advantage in terms of technology, it will need to continue innovating and enhance its product offering beyond its existing basic features.

Industry experts believe there is better technology available than apps. The digital-only platform, MyBank provides an example of how AI can generate new options for finance. By 2019 MyBank had launched the 3-1-0 model, a business loan that takes under three minutes to apply and less than a second to approve, with no human intervention required. When used in the right way, AI can reduce the time taken to make a loan approval and at the same time, ensure loans are more effective by lowering the non-performing loan ratios. Other businesses have applied their historical data from existing customers to develop a predictive model and determine the key variables that account for certain factors like missed repayments. Implementing this kind of process is not possible for new banks that lack past information.

Protecting finance data with AI

The more data acquired, the more responsibility you have. Finance data consists of some of the most private and sensitive information. It is therefore critical finance controls this data and AI delivers another layer of protection against potential cyber-attacks.

Several finance services businesses have incorporated machine learning into their security systems. Some have struggled to combat advanced cyber attacks with groups with access to their ML technology and managing their fraud detection rates, with high levels of false-positive alerts daily. Controlling false positives in financial security is a significant issue. Monzo, for example, has come under scrutiny for blocking customer accounts for extended periods because automated software has detected signs of potential criminal activity, and they lack the human staff to manage the backlog.

AI and deep learning systems have reduced this level of false positives and the overall level of fraud detection. These improvements have enabled the finance industry to focus more time on potential fraud, improving its security and enhancing the overall customer experience.

While there may be challenges and concerns with automation, the positives of giving more time to employees due to AI is valuable. In the scenario mentioned, fewer employees focusing on false positives means more satisfied customers and additional staff managing actual cases of fraud.

Whether referred to as fintech or banking, the case of managing money focuses on people and data. If data is handled effectively, people can create accounts, deposit and spend their money easily. When people apply for a loan, the process will determine that the right people are approved, and others declined, and there is transparency for both sides to understand their results.

The most effective data processes available today predominantly include AI technology, and this is the case for the finance industry.

People are beginning to adapt to AI at a steadily rising rate. It’s clear that modern technology is evolving rapidly and has had some impact on nearly everyone’s lives.

AI has become profoundly popular in multiple industries for a range of reasons. Improving efficiency, managing information, identifying trends in data are a few of the reasons why AI has grown so significantly in recent years.

The finance industry is a particularly important area that needs to be capable of adapting to meet the needs of their customers. The conventional ways of managing customers don’t necessarily work as well today. 

In the case of the finance industry, AI and Machine Learning have various applications. Chatbots, robotic process automation are good examples of AI applications in finance. Global studies have indicated that applying AI could save the finance industry over $440 billion by 2023. Many industry leaders are questioning how exactly AI can transform the finance industry and support the global economy.

Risk Assessment 

AI in finance is being utilised for maintaining important business records, in the case of finance, this could be information such as credit scores. Before customers are offered a credit card, a finance company will check multiple records, loans etc and use this data to adjust the interest rate applied to the card offers. 

This process is complex and involves multiple record checks but AI is capable of doing this work quickly by utilising data and then recommending the right product and interest rate for each customer. Human-based analysis may include errors that can result in potential costs to finance the business. AI memory is developed on Machine Learning, eliminating the margin of error.

Customer Support

Many finance businesses have launched chatbots on their websites. A chatbot managed and integrated by an AI development business is capable of interacting directly with customers and answering specific questions. This saves time and more importantly money for the business.

Detecting and Managing Fraud

The primary goal for most businesses is reducing risk, and this is particularly true in the world of finance. There has been a rising number of security breaches and scams in the finance industry and so customers are more cautious about their money. Many financial institutions have implemented AI services to detect cases of potential fraud. AI tools are capable of detecting fraud through analysis of one transaction activity. They can detect fraud by monitoring unusual transactions and location changes. With the support of AI, it is becoming more difficult for hackers and fraudsters to complete these activities.

Finance Advisory Services

Machines can apply bionic advisory tools which provide an efficient and accurate service, but industry experts believe a combination of these tools with the human mind generates the highest results. While these new technology tools can generate efficient results, they do require human intervention to generate the most success.

Financial Trading

Understanding future trends in finance are challenging and so many investment businesses use AI to generate a clearer understanding of future patterns. Machines are particularly useful in managing large volumes of data in a short period. They also can assess financial changes and detect certain flaws in a system and offer solutions. 

AI is continuing to make steady progress in the finance industry and judging by the pace of change, it will have a significant impact on the employee structure in certain roles in finance. Ultimately AI can greatly reduce the potential challenges in finance and lessen the potential of security breaches. Customers can be given better services, enhanced support and opportunities for smarter trading.

A new study from NTT suggests that over 80% of financial institutions believe AI is a vital part of differentiating their business, future success and generating new business. The study, however, indicates that only 16% of financial businesses use AI and data.

Senior financial leaders overwhelmingly agreed that the adoption of AI was a very important competitive driver of success over the coming years. While AI generates opportunities for creativity and further innovation, existing challenges are influencing the adoption of this technology. Implementing technology and requirements with organisational skills are considered particular challenges when considering AI services.

Since the pandemic, customer searches for digital finance solutions and applications has risen considerably. Today more than ever, financial institutions need to find a way to eliminate these barriers within AI to support customers and be capable of providing the support they need.

Customers display clear insights that they require banks to work as strategic partner on their financial decisions. AI offers a pathway to providing the services that customers are demanding. The data clearly shows that financial institutions need to focus on AI to meet the rapidly evolving needs of consumers, or potentially risk losing customers to their competitors. 

The main challenges for financial institutions to attract and retain customers involves using AI to offer a customer support channel to each customer, building further trust with customers, emerging competition from within the fintech industry, limited in-person customer engagement and a relatively slow rate of launching new products.

The majority of financial institutions view personalised services as an ideal opportunity to attract new customers. However, data shows that only 16% of financial businesses are using data to provide financial guidance to their customers.

The key drivers for financial businesses investing in personalised services are improving customer acquisition and retention, generating new revenue channels and improving customer connections. Financial institutions cite challenges with implementing AI because of the necessary changes needed to their business. This includes adjustments to their technology, skill changes, management support and creating a new business startup culture in an already established business.

The next stage in delivering the digital bank of the future is enabling a more comprehensive use of AI and other digital technologies to connect and engage each customer. Financial institutions worldwide need to focus on AI, big data analytics and processing power, as well as implement the necessary changes and strategic partnerships required to meet the expectations of their customers.