The global impact on food security is one of the major priorities at the COP27 climate summit. Recently the Commonwealth Secretary-General called on other leaders to collaborate and learn from each other to transform their future strategies.

Patricia Scotland, the Commonwealth Secretary-General, emphasised that for countries to deliver a more productive and sustainable agricultural industry and ultimately be more resilient to climate change, we must utilise big data and other digital technology. New digital tools can transform business plans within the agricultural chain and tackle productivity, harvesting, finance and supply chain management issues.

At COP27, Commonwealth Secretary announced a new policy guide focusing specifically on global food security. This guide is one of the first to explore how digital technology impacts the agricultural industry. Scotland believes that this policy guide is a critical step, not only for the Commonwealth but also for small, developing and middle-income nations. The guide supports policy leaders in recognising key areas that can improve and develop this market.

Agriculture is responsible for food security and employment in most Commonwealth member states, with over half of the collective 2.5 billion people residing in rural regions and connected with smallholder farming.
Created by the Commonwealth Connectivity Agenda, the framework discussed in The State of Digital Agriculture in the Commonwealth guide explores various regions based on their current digital technology, infrastructure, and enabling further digital progression and suggesting strategies for progress.

According to the policy guide, while regions like Africa lack some critical data infrastructure, considerable progress has been made through digital innovation, new technologies and services. In Asia, technologies for agriculture have progressed across the region, but overall affordability continues to challenge the most vulnerable communities.

The business development market, financing and investments remain underdeveloped within the Caribbean and Pacific Small Island nations. In Europe, Canada, New Zealand and Australia, smart digital technologies are widely used, and the policy guide encourages other regions to collaborate and learn from these innovations to assist them in making continued progress. While speaking about climate resilience and food security at COP27, Secretary-General Scotland emphasised that further efforts must be made by the public and private sectors to recognise the potential of digital and big data solutions for the agricultural industry.

A recent study by Harvard Business Review suggests companies expect finance teams to be more strategic, collaborative and automated. Increasingly, businesses are requested to provide financial details quickly and support leaders in achieving their goals. Finance professionals are, however, facing the pressures of daily administrative duties, which means fulfilling this strategic role is more challenging than expected. Many industry professionals believe it’s time for finance teams to automate tasks and start focusing on core strategies. 

Finance teams have a tradition of utilising new technologies that make the technical aspects of their jobs faster and simpler. Tech-focused businesses and associated C-suites are adopting automation to accelerate their efficiency and performance. The affordable and improved technology will likely transform the function of a finance team.

Many industries are adopting automation within their accounting teams. Businesses have automated processes delivered by their finance teams, such as financial close, accounts payable, financial planning and analysis. While automation impacts accounting services, there is still much to be done in this industry. According to a Deloitte report from 2020, over 75% of respondents said their business accounting processes are predominantly manual or require significant manual input. Under 4% of respondents suggested that their business has implemented robotic process automation (RPA), while around 2% had integrated machine learning and artificial intelligence (AI). 

Considering the existing economic and financial conditions, geopolitical uncertainties and increased inflation, finance teams are under further pressure to raise their performance levels and focus on their corporate strategy. The Harvard Business study suggested that 89% of finance teams can provide unique and valued input on business challenges. A further 83% believed there is a potential risk to their business if the finance team doesn’t contribute to the overall strategy. However, many believe that finance departments are held back by basic tasks, which prevents them from adopting this more strategic role.

Automation doesn’t necessarily remove humans completely from a process, but it enables machines to focus on repetitive work. While professionals can increase their productivity and drive key business objectives. If finance teams spend a large portion of their time on manual activities, they lose the opportunity to explore data and deliver high-value insights. By automating tasks, finance teams are in a stronger position to add more value. 

In finance, automation enables businesses to identify missing payments and remove potential errors. Automation can improve the analysis of customers and reduce or eliminate findings that can lead to poor decision-making and planning.

Applying automation to financial close

In a Trintech survey, over 50% of financial professionals said that meeting deadlines and timescales were the biggest challenges in the financial close process. Manual processes and reduced use of financial automated solutions can impact the ability to generate insights, particularly when working in a remote or hybrid environment. According to EY, over 60% of CFOs said their closing process is manual. When asked what stopped businesses from implementing the most efficient financial close, lack of automation and manual errors were considered notable factors. There is a growing recognition that manual activities and a lack of automation directly impact the challenges experienced during financial close but many are yet to have a solution to this issue.

Automation presents several benefits, but there are challenges related to the implementation process. Businesses must understand these issues and be prepared to create the necessary solutions. Before launching automation, companies must determine whether to automate their existing workflows or restructure them. Prioritising areas for automation must focus on repetitive tasks that are more likely to incur errors and recurring costs. Financial operations represent the core of many businesses, especially if these changes can be risky. Some solutions require organisations to invest considerably to implement the necessary changes to their systems. 

One of the biggest challenges to overcome is the underlying fear of employees and getting them to invest in the automation process. Some finance members may be concerned about being replaced by automation and other technology. Others may be worried that their team is piloting automation for the rest of the company. It’s critical engaging with employees to eliminate any negative perceptions of the process and highlight the benefits. 

With further technological advancements, businesses may fall behind their competition if they fail to recognise the benefits of automation. Shifting from manual to automated processes can be very beneficial, increasing performance, saving time and reducing the chance of fraud. Finance teams are now a central part of business operations. The CFO today is directly associated with key strategies. As technology becomes more sophisticated, businesses can automate more activities. 

Finance teams are dependent on the continued availability of accurate data. Leveraging data solutions and other technologies are signifcantly beneficial for forward-thinking businesses, creating more insights and widening capabilities. 

Representing data using graphics like charts, animation and infographics are regarded as data visualisation. Visual displays and representing information in this manner help communicate complex data relationships and data-driven insights in a form that makes it simple to understand and determine a structured plan. The primary objective of data visualisation is to assist in recognising patterns and trends from large data sets.

There is a significant volume of data available today, and to generate any benefit from such considerable data, real-time analytics has become critical for all industries, including finance, to gain a competitive edge. The finance industry is experiencing a significant digital transformation and growing pressure to innovate. To achieve this digital transformation is delivered in various ways, and by leveraging data visualisation, the finance industry can utilise benefits like:

A more comprehensive view into customers’ behaviour and needs

Timely financial intelligence to make informed decisions

Enhanced client reports

Innovative fraud detection

Detailed view of risks across the entire business

Some data visualisation tools in the finance industry include:

Risk reporting and analytics – integrating a range of data sources into a singular source can be challenging. This is the case with banks when reporting risks and performance figures. The main challenge is generating reports that highlight the risk areas applicable to the industry, like market credit, operation risk and so on. Data visualisation is the ideal choice in these cases since data can be consolidated in real-time from multiple sources to create reports that can provide visual analysis. Data analytics can also enable quality data checks across sources to generate error-free reports.

-Client reporting and CRM – client relationship management systems and clients’ reports go together for the finance industry. CRMs provide and help improve relationships with customers. Client reports give finance businesses a complete overview of the customer, risk analysis and other things. Integrating big data and data analytics in real-time provides visual reports with clear information required to make informed decisions and support examining client spending patterns and other variables.

Managing liquidity – finance companies must manage liquidity effectively and have real-time access to all liquidity positions such as currency, locations and relevant products. It’s critical to compare financial figures against standard ratios on an ongoing real-time basis. 

Data visualisations make comparisons simpler by providing clear visual reports and risk analysis. Integrating data receipts from other sources provide detailed information to predict and analyse liquidity. 

Customer analysis – determining customer needs and behaviour is a critical part of the finance industry and helps businesses create new products and services to meet customer requirements. By empowering finance businesses to interact with their customers, data visualisation technology can provide them with relevant and modern information to offer financial products created to customer needs.

Integrating with social media – social media is a creative way for finance businesses to market products and improve their relationship with customers. This provides big data which can be integrated with CRM applications. Data visualisation tools can connect all data sources and provide data analytics is very important for these companies. 

Enhanced identification – using data visualisation, the visual results can be produced for any data without managing filters and sorting lots of details. A range of graphs and charts can be generated instantly to highlight specific details. 

Collaborating and sharing data – The reports produced can be shared with multiple teams, simplifying data sharing within a finance business. Teams can collaborate and work together, whether it’s in the exact location or not. 

Detecting trends and anomalies in data – One of the main concerns for a finance company is determining fraud. Reports created with data visuals can help detect patterns that may be overlooked because of the sheer amount of data. These reports can help eliminate the potential for financial fraud. Many financial institutions have separate dashboards for fraud detection and risk management. 

In conclusion, data visualisation is a powerful tool and can significantly support the finance industry. By applying data visualisation services, consultants with experience within the finance industry can create a unique and competitive edge for their customers. 

With the significant advancement of technology, our lives have experienced considerable changes. By leveraging innovative technologies such as AI, ML and Big Data, we are transitioning into a new stage of innovation where industries worldwide are automating manual processes. This has made our lives similar and seamless, and the finance industry has also embraced this shift towards digital.

Artificial intelligence has emerged as a pivotal part of this digital transformation. In a report by McKinsey Global Institute, it’s estimated that utilising AI to improve core finance functions and provide customised services to customers will increase industry value by over $250 million.

A range of innovative tools is continuing to reshape the finance industry, and this is only the beginning. As we progress to the next stage of technological discovery and development, we must explore what role AI will play in disrupting the finance industry, its influence on businesses and how it will create a range of new opportunities.

The finance industry is recognising the significant transformative potential of AI. Industry analysts believe that by leveraging AI, the finance industry can save $1 trillion by 2030. Another study by Narrative Science a few years back suggested that over 30% of financial service businesses had already adopted AI-focused solutions such as predictive analytics and voice recognition services.

The emergence of innovation is predominantly focused on the customer experience. New AI-powered tools like chatbots are becoming a necessity for many new businesses on the front-end experience. Process and task automation and other analytics strengthen and elevate finance services on the back end. As suggested by Gartner, Robotic Process Automation (RPA), as an example, provides a very cost-effective service, amounting to around a third of the compensation provided to an offshore employee and about a fifth provided to an onshore employee. RPA does the manual work, utilising a rule-based system that automates repetitive tasks
AI in finance focuses on machine learning, but automation plays a significant role in banks. The finance industry has benefited considerably from machine learning. Banks can gather and explore vast amounts of finance-related data. Machine learning is a discipline of AI which enables machines to learn and progress by using data and not relying on human intervention.
Voice recognition is another modern innovation that applies AI to perform banking operations through voice commands. At the core of this technology is Natural Language Processing (NLP). This AI-driven technology is used to design a range of virtual assistants and chatbots.
In the financial scene, leveraging AI provides two distinct advantages; firstly a big increase in efficiency, and secondly, reduced stages that could be exploited for fraud. The trend of AI-focused lending initially emerged within the tech startup and was then rapidly adopted by other entities. Since market investment is mostly dominated by individual fund managers, it might be difficult to understand their influence on AI. However, AI-focused funds can considerably reduce the possibilities of human error through their ongoing evolving rules and algorithms.
Other significant factors behind the increasing demand for AI in finance include the development of cheap and efficient resources, the digitisation of financial services and the rise of new data on individuals and organisations.
The progression in advanced technology like Artificial Intelligence has transformed the financial industry. With the rise of next-gen tech applications disrupting the industry, technologies like AI and ML have significant potential to transform the sector for the better. Investment banks and financial startups are now utilising the best AI to enhance profits, maximise efficiency, eliminate errors and generate the best returns.

Automation can support business and the success of the finance function. In terms of the responsibility of CFOs, there is further recognition of the need to integrate digital measures within finance. Many finance leaders, however, focus on strategic challenges while remaining predominantly reliant on traditional systems incapable of delivering the desired results.

Any progress and evolution of the CFO is dependent on innovative tools and is capable of automating selected financial functions. Progress appears when finance shifts from silos to increased integration across the internal value chain. Automation in finance must focus on processes which eradicate the separation of silos within financial activities. Manual, repetitive tasks require automation, optimisation processes and elimination of the possibility of errors, so human intervention is limited and focused on more strategic tasks.

Through automation, financial services can adopt a more performance, value-based approach rather than being the traditional cash manager or gatekeeper. New technologies make it possible for financial processes to deliver results in near real-time to CFOs. Eliminating silos and automating manual tasks means CFOs can reshape the finance industry and their financial skills. Transforming activities means finance professionals can focus on critical business areas.

Simple automation measures for finance can include introducing onboarding performed by suppliers i.e. accessing a portal to input accurate information (which suppliers will likely do to receive prompt payments) will reduce errors significantly. AI/RPA technology can deliver faster, less human-focused invoice and purchase matching, accelerating payment approvals. Automation processes can enhance the overall AP team morale by limiting time spent focused on purchasing supplier enquiries, performing reconciliations and other compliance duties.
CFOs also have a duty to control the risk of fraud. Automation, particularly automating payments can reduce that risk. Manual activities can unintentionally create opportunities for fraud. Deploying automation can validate selected payment processes and ensure incorrect payments do not occur.

Integrating automation offers several operational benefits. Enhancing accuracy and reducing manual data entry is just one aspect. Focusing on improving workflow and process automation is another area. The benefits of automation are diverse, ranging from improved staff morale and retention to transforming finance into a value-centric business.

Failing to consider finance automation can leave CFOs exposed and limited by traditional services. Attempting to meet the strategic challenges of a business when dependent on conventional systems and manual activities can be very challenging. The future of the finance function isn’t focused just on technology, but it is a significant factor to consider. Success will depend on determining how the finance function operates and supports a business.

A new survey of finance professionals found many are unprepared to meet the demands for more insights and information, despite ongoing significant transformation efforts already in place. The national survey from Deloitte’s Centre for Controllership and IMA (The Institute of Management Accountants) discovered that 76% of finance professionals confirm their controllership functions have started their transformation journeys but nearly 95% report additional work is needed and admit progress is slow. A further 65% admit their business function isn’t prepared or only somewhat prepared to meet their future demands. 

The report “Stepping into the future of controllership: from accounting to finance”, explores the impacts of the pandemic on financial services and how finance professionals can use this period to drive innovation within controllership and generate more value for their businesses. 

According to Kyle Cheney, risk and financial advisory partner at Deloitte, a clear lesson from the pandemic is that driving digital functions within controllership is here to stay. Cheney explains that activities previously considered a part of the future of finance, such as data modelling and analytics, are now mainstream parts of the industry. Financial controllers are confident that they need to transform, but this doesn’t change the fact that there are still challenges to overcome on the shift toward an innovative, strategic and digital controllership future. Further data from survey respondents found a mix of responses between the existing and future conditions of controllership, including maturity gaps within vital controllership, enabling and domain areas. Enablers, like governance and compliance, were ranked as the farthest along the maturity continuum by 65% of finance professionals while nearly 50% reported data and analytics to still be in the early stages of maturity. 

Similarly, over 50% of respondents identified financial planning and analysis (FP&A) as a dominant area most in need of progress to meet the future demands of the controllership function. 

Over 60% of surveyed finance professionals agree that advanced maturity levels, or those considered to be integrated or optimised, will be required across enabling and domain areas to achieve the demands of controllership function in the next few years. The report also highlights actions that finance leaders believe will increase their ability to perform in a more innovative, challenging and increasingly digital era. 

Loreal Jile, IMA VP of research and thought leadership and the lead on this study, highlighted that transformation in controllership is more than adopting new technology. It also involves considering how finance teams use that technology to become more strategic partners to the business. Jile explains that the hope is controllers, CFOs, and other finance leaders can use the report as a roadmap to continue progress with their digital plans. The report can enable organisations to structure organisational silos to support intelligent, flexible and more resilient operations capable of managing future industry challenges.

Data-driven finance has become a key priority for finance and accounting teams worldwide. However, a new study suggests that only 23% feel they have the data and analytics potential required to generate real-time insights and deliver the strategies needed for their business.
The report, Behind Every Successful Enterprise, There is Data-Driven Finance explores over 200 finance and accounting leaders to determine their goals for the year ahead. Saurabh Gupta, the president of research and advisory of HFS Research, explains that the study finds that data-driven finance has become the main priority as businesses pursue further growth and profitability. The journey toward this includes many challenges, and most finance leaders feel they lack the tools, technology and talent to progress in this environment. For those that are growing fast and have reached the peak of economic performance, it is clear that focusing their investment in data-driven finance is paying off in creating more flexible operations and repositioning finance from a cost element to a more strategic focus.
Some of the key findings from the report include:

Data-driven finance is the future – Nearly 90% of finance leaders believe that data-driven finance is the future, and 87% agree that they must invest in AI analytics, cloud and digital talent to achieve their data-driven finance targets.

The second key finding from the report is that most finance teams stumble on data maturity. Only 23% of businesses already have mature, data-driven functions, while another 77% believe they are still working on creating a strategy for their financial data and analytics. On average, finance leaders think it will take two years to achieve their data-driven finance goals.

The third finding was that the primary drivers behind finance teams desire for data-driven finance are identifying growth opportunities to support business and become a more strategic advisor, as well as reducing operational costs and improving capital allocation.
Of the fast-developing businesses with higher growth rates, 36% have mature, data-driven finance functions, and more than 30% believe the main driver of their data strategies is the ability to be a strategic advisor for their business. The majority of fast-growing companies are actively developing core centres to improve the management of data and analytics. In contrast, only 23% of mid and slow-moving companies are developing these core areas.

With the current economic and geopolitical conditions, technological disruption and continuous changes in consumer behaviour, the finance function has become a critical area for intelligence and supporting corporate strategies. To capitalise on this intelligence, finance teams require sophisticated data and analytical tools that provide them with real-time insights and the ability to determine varying scenarios. Many emerging companies have managed this feat and applied sophisticated data-driven finance functions, but others still have a long way to go.

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.