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.

Deloitte released new data that suggested over 82% of hiring managers for financial-based positions at public companies believe talent retention is a significant challenge, compared to 68% of hiring managers at private companies. The response from hiring managers at public companies also emphasised the efforts needed to attract and retain employees, while the findings were lower for private companies.

Matthew Hurley, senior manager at Deloitte Advisory, explains that there could be several factors such as company and team size, the complexity of work and even location that could create this gap. Private businesses highlight similar issues to public companies, but a lesser extent. The three drivers influencing hiring over the next 12 months include:

The need for additional headcount in existing areas where workloads are increasing. This could be partly due to a rise in regulations. For example, ESG reporting requirements have increased workloads for some members. Various standards have been released in the last few years and increased the workloads for finance professionals.

The second factor is obtaining talent with technological skills. Many large businesses are experiencing major finance transformation projects. New technology, including AI and machine learning, is rising rapidly. Companies are exploring ERP (enterprise resource planning) upgrades and updates.

Attrition is created by the Great Resignation. While the study by Deloitte suggests hiring managers believe many employees were leaving for higher pay, there were other impacts such as respondents from public companies moving for a better title and employees deciding to change industries. 

Hurley explains that this isn’t a problem that can be solved solely by investing money. We need to think carefully about what is vital to our people. The battle for talent is ongoing in many industries, and finance has been impacted considerably. Hurley believes it isn’t necessarily a decline of interest in finance. Hurley highlights that discussing the talent challenge with CFOs is finding talent with a mix of finance and technological capabilities is creating existing challenges.

There is also a drive to keep the younger generation interested in finance careers and provide them with the right skill sets. More education institutions are exploring ways to engage with finance leaders to determine what training they need to provide to ensure their graduates are successful. Many universities are taking courses in machine learning, robotic process automation, new analytics and data courses.

A report published by UK Finance has highlighted how cybercriminals have shifted their focus toward consumers and their personal and financial data. Losses to authorised push payment fraud, where a person makes a transfer to an insecure account, exceeded card fraud for the first time last year. UK Finance recorded a 71% increase in this type of fraud, with losses exceeding £355 million, compared to £261 million from card crime.

Open Banking Excellence (OBE) focuses on knowledge sharing, new thinking and collaboration within the financial services industry. Fraud is predicted to continue rising and is something the finance industry will need to face head-on. Michael Huffman, the Director of Fraud at GoCardless, explains that we have shifted into a challenging economic environment that will cause individuals who wouldn’t have considered fraud as a possible mechanism to increase revenue. Huffman believes lots of data and information can be made available to provide customers and clients with added security.

Mike Haley, the CEO of UK-based fraud prevention agency Cifas, explained that identity fraud has increased by nearly 40% in the first five months of this year, and false applications for bank accounts have increased by 60%. With Open Banking, there are opportunities to reduce fraud through mechanisms like monitoring bank account information to verify customer identities.

Open Banking has been a great success in the UK, but Europe has adopted a range of specifications. There isn’t a standard testing process to see how well it’s implemented, so industry experts believe the regulatory system needs to expand as we move toward Open Finance.
With APIs, encrypted data transfer and reduced information sharing, security lies at the core of Open Banking. As the finance ecosystem expands, its inevitable fraudsters will create new challenges for the finance industry. Industry analysts emphasise that while open banking hasn’t created new fraud typologies, it has increased what is referred to as the attack surface i.e. the volume of entry points for fraudsters to access information to initiate payments or intercept personal information.

In the UK, we place a lot of emphasis on creating a trusted framework. Implementing an Open Banking standard is vital to the protections put in place. If done correctly, Open Banking can provide several ways to fight fraud, but there’s another important area to consider – due diligence by the customer.

Over the last year, there has been a shift from unauthorised card activity to APP scams. Industry experts believe this is partly due to the success of security in design, meaning the weakest area in the system lies with the customer and an individual pushing out a payment.
Customers must be cautious because you can’t legislate against a lack of attention. There are layers of protection, but ultimately individuals need to take control, particularly with payments.

Despite these challenges, the industry is in a strong place, and Open Banking continues to develop a resilient security model and a trust framework for its customers. Industry experts emphasise that when it comes to fraud and financial crime, there should be no competition. It should be about sharing data and intelligence and supporting by educating customers.

Big data is critical to the ongoing progression of financial services, but many businesses in the industry are failing to utilise its full potential of this industry.

Many financial businesses have harnessed significant volumes of new data as a result of adopting digital technology, but most have barely begun to gain the full value from the information available to them. According to a study by Seagate and the International Data Corporation, the average business collects and measures approximately 24% of the operational data available to it. There are several reasons for such a low figure, ranging from hesitancy regarding user privacy and regulatory compliance to challenges acquiring all the appropriate material in the necessary format for assessing data.

The first major challenge facing financial services businesses looking to make better use of their data comes back to their data ecosystem. The second challenge relates to the available talent, and the third is data management. The sheer capacity of the cloud is tackling the first challenge. Cloud-based technology has also created further possibilities for data and analytics teams, but industry experts believe that despite the advances in managing and governing data, there are still opportunities to improve the process. Most of the focus has moved from cross-functional platforms that allow businesses to make the most of their data.

In terms of the talent challenge, industry experts highlight how the sector is struggling to attract more people with the appropriate IT skills. Nick Broughton, the CIO at Novuna, believes that technology alone isn’t capable of generating all value; the industry also requires data-capable people with new and innovative ideas. Data science skills especially are critical to delivering real insights from the large volumes of data we have available. Attracting, retaining and growing internal talent around these important skills is another challenge when the demand in the market is rising.

A recent survey of financial businesses discovered that over 80% had struggled to hire data scientists, despite average annual salaries often exceeding £100,000. Over 25% of respondents suggested they didn’t have the necessary skills needed to achieve their commercial requirements. Considering these figures, the industry will need to become more flexible with employment policies and practices if it wants to attract and retain the data specialists it requires. Based on the high demand, these data professionals have the power to dictate the terms. Many people prefer to work remotely and flexible hours, so employers must be prepared to meet these demands.

Some businesses are going to greater lengths to create a reliable pipeline of potential talent, building networks with data science communities and creating special training programmes.

In terms of the other challenges, businesses are working hard to put more governance systems in place. The overall aim is to create a holistic view of their services and customers using data gathered and managed in real-time. It’s important to make tools accessible to everyone in a business, not just a handful of IT specialists. One opportunity emerging from this work is that it enables businesses to create new ways of meeting clients’ expectations. Ultimately, delivering the best customer experience is pivotal to any data-driven plan.

Creating a personalised service is one important area of development, but the potential of integrating augmented services, and combining data insights and human interactions is exciting for some businesses. For example, by using a range of data, companies can create investment signals and intelligence that managers can use to improve their relationships with clients. With the help of AI tech such as machine-learning systems, client-facing employees can determine trends that wouldn’t be possible. New tools can help customers understand their financial health and the risk profile of investment opportunities available to them.

The future is likely to involve clients using tools for experimentation. AI tools can show how investments could change over time but we find ourselves in a stage where regulation is failing to maintain pace with technological progression. Financial businesses must be cautious in their approach toward AI-enabled services, to maintain customer trust. Specialist positions such as data-ethics managers ensure an approach to AI is transparent, unbiased and capable of delivering the best outcomes for clients. Financial companies are already using chatbots and other virtual technology supported by natural language processing. Natural language processing also allows businesses to perform automated searches of various sources which can identify certain problems such as profit warnings or greenwash. Applying alternative data sources like smart sensors for climate-focused investments is likely to become more common in the future.

As the world moves toward data as a product, we need to start developing services with the data they serve. Making data services simple, secure and governable will be critical to the success of plans.