Digital transformation is empowering the finance industry to enhance business agility and prepare for future developments. In the recent Finance 2025: Digital Transformation in Finance report, Deloitte explained that automation, cloud-based and cognitive innovation are enabling finance businesses to transform and simplify activities.

Automation will allow finance-related businesses to improve their costs by restructuring their workflows and how they’re processed. Those businesses reliant on traditional technology and hesitant to adopt new systems are potentially at risk of reducing their progress and being capable of offering the best user experience.

Other companies have embraced digital transformation and utilised new solutions to meet the changing demands of their customers. For large-scale organisations this can be challenging and is often why companies are hesitant to integrate new technologies. Ageing architectures can hinder the ability to adopt new systems and utilise the full potential of the technology available today within finance.

These typical challenges highlight the importance of creating business agility in pursuing new customers and improving the overall end-user experience. The ability to connect with people and deliver financial services to more individuals while meeting customer demands requires a consistent and reliable service. There are many options emerging for finance companies. Still, for anything to happen, businesses need to ensure they have the agility to deliver the right services to the right people at the right time. 

These challenges are typical for larger, incumbent finance businesses, due partly to the rising competition from smaller, agile fintech companies which lack the limitations of large, legacy infrastructures. Mobile banking is playing a vital role in changing the way finance businesses engage with their customers. According to research from McKinsey Global Institute, digital transformation represents some of the most important ways of driving financial inclusion. Smartphone digital payments, for example, have transformed the customer experience, resulting in increased expectations surrounding the broader user experience and encouraging companies to accelerate their digital transformation plans.

The rise of emerging technologies such as big data and IoT is also accelerating the transformation toward intelligence and cloud technologies. The number of data businesses generates today is increasing and managing large volumes of information has driven greater use of AI and other technologies. Financial institutions that maintain pace with the rapid changes of digital transformation are likely to be the ones that reap significant benefits. Companies that fail to address the importance of digital services could lose their competitive advantage.

IT tech and services company Accenture believes that fintech-driven banking is the path toward successful talent acquisition and retention, as more businesses face rising pressure to remain digitally competitive and avoid the potential of losing customers and market share. 

 

As AI continues to present new opportunities, the finance industry is putting its potential to good use. Predicting future trends, however, comes with its challenges. There are clear benefits of using AI in finance, but there are risks associated with implementing new technology.

AI improves financial inclusion by ensuring banks can determine credit scores, which is a critical factor in money management. AI can draw on social media or other sources to understand the ability of people to repay a loan. Reducing the constraints with financing means institutions can focus their efforts on better access to finance and growing the economy. ML and AI models in finance utilise big data to generate accurate predictions about the market. They assess multiple risk factors and determine the investment performance against various industry and economic scenarios. This process reduces the overall investment risks for finance businesses and their customers.

AI also supports investors in generating insights from multiple areas to develop their investment strategies within a relatively short timeframe. Several research groups are discovering that AI-based investments are exceeding the performance of conventional ones. AI and ML can improve efficiency and inclusion, but they also have two main risks.
AI-based credit scoring models may cause unfair lending processes. While a credit officer will be cautious not to include gender or race-related factors in scoring, ML may mistakenly consider these factors. ML models are only as reliable and accurate as the data they are made with. If models consist of poor data or data that reflects core human prejudices, it may generate inaccurate results, even if the data generation improves. The second challenge is that algorithms can also make finance businesses vulnerable to cyberattacks. It’s easier for cybercriminals to take advantage of models that all activities in the same way, compared to human systems, which work independently.

Policymakers need to accelerate their resources to combat the risks related to AI and other technology. One important method is improving the overall communication process. For example, finance-related businesses should instruct all users if a particular service uses AI. They should also explicitly identify the limitations of AI models so customers can make their own informed financial decisions. This process creates further trust and confidence and promotes a safer integration of new tech like AI.

Furthermore, policymakers should highlight human decision-making over AI-focused decisions. This approach is especially relevant for high-value areas like money lending, which can have a significant impact on the customer. Customers will feel more empowered in this scenario which allows them to adapt to the outcome of AI models. Users should have the option to opt out of having their data measured within AI models. Over extended periods, these measures increase the level of trust in new technology, like AI and ML.

Policymakers need to ensure that finance-related businesses test AI and ML models before implementing anything to remove possible bias. Testing allows businesses to check that the models are operating as expected and are meeting current rules and regulations. AI and ML can help finance businesses create a more accurate forecast of financial markets, but it can’t be considered more than a forecast. New technology like AI and ML should be viewed as tools with considerable potential if all the associated challenges are dealt with correctly.

It’s fairly clear to most now that the continued advancements in analytics will have a profound impact on the business world. The Big Data Analytics market is anticipated to exceed $225 billion in the next few years, and according to LinkedIn is a major driver of new job opportunities worldwide.

Advanced analytics, machine learning and AI will transform every part of our lives, from business innovation and government plans to our health, wellbeing and the environment. We often perceive big data and AI as technical fields but is heavily interconnected with our lives and nature. Big data and analytics is driving changes in multiple markets, enhancing R&D and improving healthcare systems.

Considerable investment and energy go towards developing AI and analytical technologies. Venture capitalist investment in AI-targeted startups has expanded by over 20 times to a value of around $75 billion, according to the Organization for Economic Co-Operation and Development (OCED). Investment is quickly expanding to new industries from transportation and construction to retail and financial services. Sooner or later, everyone will need data analytics within their business management plans.

The challenge is a lot of time and energy is being directed at the technology, there is less focus on investing in talent. To meet the rising demand, the world will require additional people with STEM skills, especially those with experience in data science and advanced analytics. Demand for data scientists has grown significantly in the last few years. Data Science and ML jobs represent five of the top 15 fastest-growing job areas in the USA, according to LinkedIn. There is, however, simply not enough young people moving into these industries, despite the lucrative salaries and career options. This is particularly true in the case of younger women.

According to Cornelia Levy-Bencheton, author of Women in Data, believes the industry is underutilising women in data science. Women make up 57% of undergraduate students and 60% of post-graduate students, but only 35% follow their studies in STEM. In the US, women represent 56% of the total workforce, but only 25% work in technology. The number is even lower within the data science area. One of the main issues is the lack of role models and the representation of women in senior-level positions.

Any plans or discussions concerning the future of business analytics and data science need to incorporate gender representation. It’s clear we need more data scientists, but more importantly, the industry requires a diversity of viewpoints and ways of creating new solutions with data. In a society where AI and advanced analytics will become vital in driving creativity, customer experience and innovation, the business equipped with the most data scientists is likely to have a competitive edge, but the one with the most diversity of skills and opinions will come out on top.

Having a mix of viewpoints, skills and opinions are important to the industry of data science. The data scientists are what matters the most and their ability to tackle problems and determine what questions need to be asked about data to deliver the most effective insights.

Gender diversity will impact the industry as the more women in the field, the greater the volume of perspectives and knowledge will be for generating new value and solutions. In an industry where 80% of big data professionals are men, more diversity can only improve processes and enhance the ability to utilise large data sets effectively.
Data skills need to be interconnected with other subjects, such as economics, engineering and robotics. The majority of future careers will require some STEM skills and knowledge of computer science. Despite recognising this importance in STEM, most students fail to take STEM classes or focus on computer science. There needs to be integration with education, the community and general awareness. These areas are creating economic and gender gaps within big data.

Aside from the obvious barriers, there are other personal factors like confidence and participation which influence the uptake of data science roles. Studies suggest that most young women are interested in STEM careers, but very few pursue this further into later stages of education.

Industry experts highlight that we require more women as role models to encourage young female professionals to feel more confident that they can pursue a career in the data and analytics market.

A recent survey by financial and HR software provider, Workday, suggests that CFOs are actively looking for financial professionals with specific skills in AI and ML platforms. CFOs are eager to invest in new tech to attract and retain the best finance talent with artificial intelligence and machine-learning skills. Workday released its Global CFO Indicator Survey, suggesting that nearly 50% of all CFOs intend to invest in new technology services to attract future finance talent within the next five years. A total of 57% stated that are looking for new hires with AI and ML skills or experience. 

The survey consisted of hundreds of CFOs in Australia, Asia, the US and Europe. CFOs are looking for new and creative skills in new talent to strengthen their workforce. Over 40% of the respondents explained that they are focused on analytics and data-storytelling skills within their new hires. These are areas that weren’t necessarily a focus several years ago. Data-confident CFOs represent those that can transform data into critical insights. AI allows CFOs to spend less time on measuring data and more time on explaining what value big data has for a business. 

CFOs are actively looking for individuals who can utilise AI or ML to acquire this information from their data and then communicate the key insights. Telling the story is particularly important, especially if someone cannot explain the data, then no one will listen to you. According to Forrester Research, one in five companies will double down on AI to increase the delivery of their business insights. This year, the reliance on real-time technology, combined with is forecast to increase by 20%, eliminating the inactivity between insights, decisions and business results. Forrester believes the AI market will grow from a current market value of $25 billion to $37 billion worldwide by 2025. Of this study, 15% of non-technology based companies believe they will design and test talent in their AI teams to develop AI-focused services as the technology uptake continues to expand. A few years ago, only the highest tech players were investing in design for their AI efforts. This year and beyond, many non-tech firms will follow a similar path to the likes of Google, Salesforce and Microsoft and allocate a design leadership team for their AI projects. CFOs are using better data management services and upskilling teams to avoid the data skills gap. Nearly 60% of those surveyed by Workday believe their potential to convert data into insights is very high, putting them into a category that Workday would refer to as “data confident” CFOs. 

CFOs and other financially-related members are still far behind other business leaders in terms of utilising technology with customer-focused systems that generate information and data from spreadsheets and data sets. What is critical to many leaders today is having a technology that is simple to use. If it is easy to use then people can focus their time on the important and more creative aspects of their work. 

The Workday survey suggested that 48% were actively investing in customer-focused systems for finance-employee tasks, such as automated accounting and financial planning and analysis. Nearly every CFO in the survey agreed that technology updates would become even more vital for attracting and retaining talent. 

The pandemic has transformed the expectations of CFOs. Today, professionals require reliable information quickly. Using AI and ML to detect unusual patterns helps businesses analyse data and determine what the figures are saying. Attracting, upskilling and retaining talent remains the biggest priorities among those in the survey. 

While technology has become a predominant focus, CFOs continue to be equally focused on the need for diversity and inclusion and ESG from an investment and supportive aspect. It’s an exciting time to be a finance industry professional. Technology empowers people to apply the skills they trained for, and CFOs are actively looking at ways to invest in these new skills and technology.

The rise of open banking is transforming how people use and interact with their finances. Open banking enables financial providers to offer more flexible and varied services for their customers. Open banking allows customers to share their financial-related information with authorised third-party providers. These groups can use this data to create a more bespoke service. In other words, open banking is a regulatory system that drives innovation and competition within the finance market. Using information effectively will encourage banks to deliver better services for their customers.

In Europe, open banking is focused more on expanding the traditional banking sector and increasing competition between the existing banks and new fintech companies by applying more customer data. People are requesting more open banking options as they want greater control over their data and broader access to various services that meet their requirements. A rise in these services results in more competition and better deals for the customer.

People are more aware of how businesses manage their personal information and demand a customised service from the finance industry. The rise of third-party systems is making customer lives easier by delivering specific services that meet the needs of each individual.

Big data is transforming the way financial service providers operate today. Measuring large data sets allows businesses to make quicker and more informed plans about their products and services. Big data has enabled new types of financial tools to develop that was not necessarily an option in previous years. The benefit of integrating big data across multiple verticals will be critical in the continued success of open banking.

Open banking offers several opportunities for small and large businesses. Sharing customer data means companies gain a better understanding of their customers. The continued rise of open banking will likely influence how businesses operate shortly. Those who recognise and apply the opportunities available with open banking are likely to be the ones that succeed in years to come. As payments become more focused on data and more personalised, open banking could potentially deliver new opportunities, enabling customers to connect directly with their bank and authorise transfers without leaving the mobile or online app. This type of example highlights how critical open banking will be in connecting customer data and providing an integrated and individual payment experience.

Big data is positively impacting the fintech industry and is likely to continue for some time. Finance companies who want to remain competitive will need to utilise big data and open banking to deliver the best available service to their customers. Some of the leading established businesses in the industry are acquiring or partnering with new fintech companies to remain competitive. For example, Visa recently purchased Sweedish-based fintech startup Tink, with only 400 employees, for a little over $2 billion.

The transition to open banking is happening and will play a significant part in the future of fintech and business activities. Taking advantage of the opportunities available in open banking can allow businesses to gain a considerable competitive edge. At the current rate of development, open banking will likely continue to spread across finance into other industries and quickly become the norm.

The protection of financial performance has always focused on numbers, and today, big data and automation are enabling finance leaders to take key performance indicators to a higher level. While the acceleration of new data has generated more opportunities to improve KPIs, managing that information and converting it into clear and actionable insights has proven to be challenging.

The challenges are particularly severe in businesses with data frameworks spreading across multiple systems. These tend to include gaps in data and inconsistencies in the form and quality of stored information. To utilise the best data-driven performance, finance businesses must first focus on ensuring the necessary information is captured and that any data plans fit with their key financial strategies and overall business goals. This process boils down to data governance and establishing who owns the data model.

Before considering what insights and value can come from the data, a fair way of getting that data into systems and governing for effective use needs establishing. There is great potential in leveraging data and analytics to enhance financial performance, but without clarity and truth, businesses can potentially get stuck in a constant cycle of continuous reconciliations and inaccurate data integrity that reduces the overall value of data to a business.

Governance needs to be the initial priority before considering the insights and value that can be extracted from data.
Traditionally, the IT department would have the bulk of responsibility for the data area, but lacking a complete understanding of fiscal KPIs can result in inaccuracies and unproductive work. Finance needs to have some form of ownership of the data model, along with the IT section. Finance has a strong understanding of the definitions and calculations of financial data. The capability of leveraging financial data can enable businesses to progress and keep time spent and costs to a minimum.

While most businesses are still in the early stages, automation is becoming a vital element in finance processes, such as leveraging technology to scan invoices and automating other accounts payable processes. For example, Workday combines weekly employee engagement reports with attrition data, then implements AI and predictive analytics to create adaptive planning financial forecasts.

The entire process takes time, and finance businesses should acknowledge that automation is challenging to integrate. If the information fed in at the beginning is poor, it is more likely to end with poor results. Companies need to invest time in ensuring they have the correct measures at the beginning of the process to allow everything further down the line to be clear and of high quality.

We are entering a new level of intelligence, but many businesses are yet to harness the potential of AI. Big tech companies have been data-focused since the beginning. Smaller businesses with more conventional foundations, however, weren’t built with the capability to utilise AI in their daily operations. Until now, utilising such potential was completely out of reach.

What is evolving within the intelligence space for businesses is a new element of AI designed for the commercial environment referred to as Decision Intelligence (DI). This innovative technology supports companies outside of the tech space in generating AI-driven decisions through every aspect of the business, from supply chain to marketing. 

DI is expected to support more companies with harnessing the potential of their data and to make more informed and accurate decisions. Gartner predicts that over a third of larger businesses will be applying DI within the next few years, and it makes sense that the commercial side of AI should be more focused on the decision-making process.

DI is regarded as a significant step from hoping a decision will create value for an organisation, to knowing it will generate positive change. In previous years, used historical data to assume good forecasting, pricing or marketing decisions. In the era of DI, real-time data becomes critical to the decision-making process, and we can be assured of the outcome.

In this new stage of business, data teams are not hidden away within an organisation. They are an important part of consistent communication with the commercial side of the business, utilising information from every department and converting this into immediately actionable insights and recommendations. Today, we are seeing more workforces where every employee, from all levels, is empowered to use AI in their daily decision-making.

What steps need to be taken for businesses to adopt and embed DI? There are three key areas to consider:

• A prepared AI data sets
• Intelligence fit your specific business requirements
• A platform available to all members of the business that enables non-technical teams to utilise and engage with the information and its outputs

For many businesses, developing these stages is challenging and many industry professionals believe there will be an increased demand for off-the-shelf DI platforms in the coming years, similar to the progress experienced with CRMs.

In the early 2000s, approximately 80% of businesses were developing CRMs in-house. Today, the majority of companies would never consider taking this approach. Businesses are focused on time and value, investing in designed solutions, and DI is ready to follow a similar path of innovation.

Like all technology, big data is progressing, and as we start the new year, it is an ideal time to explore what opportunities exist and what areas need improving. This year represents a critical time for big data, AI and analytics, with more businesses anticipating progress and better results for their organisations. Here are some of the areas to consider in big data this year:

Creating a data retention policy

Many businesses have overlooked discussions concerning big data retention or failed to make time to tackle this area. With global data expected to increase considerably in the next few years, and big data making up the bulk of that information, 2022 is the time to create big data retention policies and disregard the data not needed.

Defining the role of big data in the wider data landscape

To establish information across an organisation and ensure data is available for everyone for analytics and decision making, IT teams should ensure big data and other structured data in a business connects and links to all areas.

Utilising additional no-code analytical applications

Using no-code reporting tools for analytics and creating additional reports quicker for end-users and reducing work pressure on IT teams.

Reassess the true value of current applications

While it’s a positive step to launch analytical tools, businesses must ensure it works as well for the organisation as it did a few years ago when it was first introduced. Businesses are evolving, and it’s likely requirements will change in terms of what analytical solutions a business needs now compared to a few years ago. This year it would be beneficial to review the effectiveness of existing analytical tools, measure their performance and assess whether they are meeting the needs of the business.

Create an application and data maintenance strategy

As with structured data and other systems, those utilising big data and analytics require consistent maintenance. Yet many businesses implementing analytics and big data lack any structured processes for maintenance. Big data and analytics have reached a level where maintenance processes are needed.

Upskill and Training for IT

To support big data and analytical operations, new IT skills are necessary for IT professionals. Training may include further development on data science, analysis, big data storage and focusing on skills with new tools, such as no-code analytics.

Assess privacy, security and trusted sources

Big data can be acquired from several third-party sources. These require constant reviewing to ensure they meet corporate security and privacy guidelines. This review should also apply to internal data within a business.

Measure vendor support in big data and analytics

Many vendors provide big data and analytical tools but do not offer the support required for a business. It’s vital to work with vendors that generate sufficient support for your team and additional guidance for important projects.

Ensure your big data and analytics supports the overall customer experience

Nearly every business is committed to improving the customer experience. A core part of this process is establishing customer-focused automation and assisting customers in getting requests, questions and answers. Automating customer-focused systems that use NLP and AI to understand customer behaviours and engage in conversations are still in a stage of development. Businesses that focus on enhancing NLP and AI performance within these areas will undoubtedly see the benefits in the future.

Leaders must review big data and analytics plans

As these technologies have matured, it is now time for senior leaders and other stakeholders to reassess the progress of AI and analytics and ensure a business has to secure support from the top.

As we’ve experienced over the last few years, change doesn’t necessarily make everything better, but it has been necessary, and those that fail to embrace it risk being left behind. The global pandemic has accelerated the UK economy to its limit, highlighting the importance of many processes associated with the finance sector. While many innovative and flexible businesses have adapted to these changes, traditional companies are yet to make impactful changes to their organisation. 

As we edge closer to 2022, many companies are still reliant on spreadsheets, and finance professionals continue to use outdated systems to perform critical tasks. Many of the financial and procurement processes we have depended on for so many years are simply not sustainable. The rise of digital automation and other technologies has replaced many traditional practices, generating several changes to how finance teams operate. As we approach 2022, several key areas are likely to impact how businesses operate: 

Remote working

The shift towards remote working – The movement towards remote work left most businesses moving away from on-premise technology and exploring cloud and SaaS services that would enable their teams to operate more effectively. While the rapid rise of systems like Zoom and Slack have made communication easier for working remotely, it hasn’t necessarily supported all areas within finance, such as processing invoices and spending management. Financial processes tend to be updated last, which is okay if processes are running smoothly, but in certain cases it can become challenging. With the shift to working remotely, businesses lack insights into what their employees are doing in terms of financial activity. Providing clearer information over business spending and expenses is important, and automated systems can eliminate the hassle connected with managing a decentralised team. 

B2C and B2B purchasing 

E-commerce has become the main channel for customers and is emerging as a vital platform for B2B purchasing. Employees want to make purchases in stops they know and trust. The remote working environment means alternative payment systems are needed to enable staff to make necessary purchases when required. Utilising a purchase to pay platform means employees can visit the stores they want when needed and eliminates the tedious end of month receipt process, replacing it with a real-time payment system that is critical for financial reporting and managing cash flow. 

The progression of fraudulent activity 

Cyberattacks have resulted in significant financial damage to businesses and have only heightened with more staff working from home. With many employees cautious in returning full time to the office, businesses will need to find a solution to tackle fraudulent activity. Automated processes for payment processes are improving the time spent and reducing the amount of fraudulent activity in the finance area. The digitisation of financial processes creates visibility and ensures all information is correct, eliminating the need for extensive manual tasks. So not only are businesses more protected, employees can rely on a system that checks all their information in real-time. 

Cash flow remains the biggest priority 

With furlough schemes now complete, people are questioning how much insight do businesses have in terms of cash flow for 2022? To protect cash flow for the next year, businesses need to tackle operational efficiencies within the finance area. This process includes utilising live reporting, digital invoicing and automation to generate real-time information on their cash flows. Visibility on company expenditure is critical for effectively managing finances but is challenging when employees work remotely, and businesses continue to depend on monthly expense reports. 

Gaining day-to-day insights on expenditure, businesses do not need to keep large cash reserves. Historical spending analysis provides a clear view of purchasing, with the opportunity to save money on various services. 

Looking ahead to 2022 

For companies looking to stay prepared for further disruption, improving the technology in the finance area should be considered a top priority. Automation is now a big demand for most finance leaders. Instead of using robotics to replace employees, the technology is replacing traditional, time-consuming processes that should be eliminated from people’s day to day work duties. While we have experienced a shift in our working environments, it doesn’t mean we lose touch with finances. Cash flow and expenditures are still vital areas, and staff need to be equipped with the right resources to generate real-time information. Next year will be a crucial time for finance, and businesses will need to ensure they are prepared and resilient to potential industry changes.

Before the pandemic hit, senior leaders had a relatively clear vision of how the future workplace should progress for their business. While measures and strategies vary from one to another, the consensus was that action plans would happen primarily in the office environment.

Over the last year, many businesses have firsthand experience of the benefits remote working has compared to the traditional office working environment. These benefits have resulted in the growing development of hybrid working. Due to its recent success, businesses in the finance industry have begun incorporating hybrid working and considering its benefits towards productivity, employee engagement and agility.

Core data is pivotal for the hybrid workforce

Over the last few years, we have experienced advancements in data analysis and improved data capabilities. With the disruption to the working environment, businesses have voiced their concerns about which individuals or positions can remain remote. The rise of new data means businesses can approach this transition with more confidence and ensure they prepare for a more hybrid and remote working environment.

Areas to consider within hybrid workforce data:

Capacity Planning – The first area of hybrid data for finance businesses is measuring its workforce capability and availability for work i.e. who is available to perform particular jobs and whether they work remotely or in the office. This data is critical to minimise the shortage of resources, lack of capacity and a resulting impact on customer experience. It’s not only about availability but also whether people are ready with the necessary tools to operate remotely. The transition to hybrid remote working in finance requires resources, training and additional support.
Time – The next area to consider in terms of data and informed decisions focuses specifically on time. Remote working means people will inevitably be working at different times, balancing childcare or other personal commitments. Finance businesses can make more informed decisions when they understand how working hours translate into productivity and use this information to determine where people work, focusing not on work as a place but more as an activity.

Analysis of work performance

Approaching overall work performance depends on having sufficient information from an employee and manager perspective. Businesses with a good concept of their current workload and future expectations can utilise the skills available to manage their workflows.

Hybrid working creates multiple possibilities

Hybrid working presents a range of opportunities but implementing it incorrectly in the wrong manner can result in massive costs for finance businesses. Getting it right, however, can create multiple possibilities. The difference between getting it right or wrong is very dependent on making decisions based on vital data trends.

Implementing working data trends, regardless of where the work is happening, reveals insights and information for use in the world of hybrid working. The challenges of implementing hybrid remote working resulted in many businesses in finance feeling unprepared for the new normal. Many lacked the required systems or had developed the skills needed to support this workplace shift. Today, finance businesses can potentially capture data, assess it and use it effectively for their employees, customers and the future success and competitiveness of the organisation.