Coaching professionals in the finance industry believe that finance transformation will become one of the most in-demand skills over the next few years.

As roles continue to evolve after the pandemic, many new skills are becoming more in demand for finance professionals. Having specific knowledge of recovery management, innovation, new technology and building agile finance transformation plans are some of the skillsets emerging.

The strategy adopted for hiring these particular skill sets is complex. Some positions are likely to be required on a more regular basis. Finance professionals are seeking people capable of finding solutions and supporting further growth for the long term.

On the other side, businesses that recognise their finance transformation will take several years are hiring professionals with the necessary skills on a contract basis, recognising that the role will be more short-lived. In other words, there will be individuals moving into positions that will only be required for a short time and, once completed, will likely move on to another business and possibly do a similar job on a larger scale.

The concept of a technological transformation in finance was discussed back in 2018 when Deloitte released a series of predictions relating to digital technology for CFOs, assessing how the finance world would change in the future. Deloitte explored what finance leaders doing and what technology was available and asking exactly how finance could add more to the success of a business.

One prediction from the report was that the proliferation of APIs would encourage further data standardisation but that many businesses would find it challenging to manage and clean up their data.

Many companies fail to implement all of the necessary steps to align and integrate data, which ultimately means, they miss all the potential of this digital transformation.

Finance analysts don’t believe businesses are going to run out of opportunities within finance transformation anytime soon. Two of the main skills required in the coming years is finance transformation, combined with the appropriate data analytics skills to maximise the increased volume of data produced in the future. Individuals that are well trained and have strong knowledge of finance transformation will be very much in demand.

The combination of digital technology with the rising demand for talent are two trends reshaping the finance industry. Businesses are facing challenges of transitioning to digital and the importance of the finance function in this transformation.

The challenges faced during 2020 created the impetus for many to adapt and integrate new digital services that appeal to their customers. The function of the CFO has been critical in driving this digital transition. IT, strategy and business leaders are vital in assessing business opportunities and ensuring the most efficient allocation of revenue and capital. The finance team consistently adapts processes to ensure the business remains focused on transforming rather than repetitive traditional tasks that hinder overall performance.

The function of the CFO has also had to embrace its digital transformation. By reshaping particular areas of the role with a digital-focused approach, businesses can leverage the potential growth of an organisation.

The pandemic also showed the importance of real-time data and the speed and agility of analytics. During Covid, many businesses responded quickly to technology investments, allowing employees the right tools to perform vital functions. Companies had to show resilience and have a mindset that focuses on innovation and technology. CFOs are encouraged to leverage creative and innovative digital communications to enhance connectivity and engagement, generating new opportunities and increasing dialogue within the business.

Data and technology progression will remain important in delivering continued progress in finance, enabling businesses to attract and retain the best talent, support decision-making and provide greater financial transparency. Businesses need to maintain a check on changes through their culture, talent leadership and innovation in technology. By creating clear expectations, strong talent leadership, and continuously evolving through technology, the function of the CFO will become even more critical, and a business will experience accelerated growth.

As the industry transforms, there will be winners and losers associated with the fundamental changes in the finance industry as a consequence of big data. Rising competition, combined with the breaking down of traditional measures replaced with alternative systems, will impact the businesses we see today.

Customers will likely benefit from enhanced products and services, and businesses will be more capable of managing risks and overall efficiency. However, there is the potential that some customers may be excluded from selected markets and will inevitably experience exposure to new risks.

In a recent study, the Institute of Chartered Accountants in England and Wales (ICAEW), explored how the finance industry has changed because of the increased availability of new data. Unlike other businesses, employees in the financial services industry will experience the value of big data. While customers can decide which companies to purchase from, financial services are a critical part of our daily lives. The collection and use of data are valuable to the industry, meaning there needs to be careful consideration of the ethics involved.

The growing reliance on data enables customer behaviour to be shaped by digital technology, which feeds into the supply of data. Plans and decisions regarding big data have a direct social impact across the entire finance industry. As customers become more sensitive and aware of data in business, financial service providers will have to ensure they maintain their responsibilities. 

Several principles can support financial businesses with managing and resolving any ethical tensions.

Accountable for Big Data

Big data and new technologies are increasingly important to businesses today, but industry experts have pointed out that many organisations lack the skills and experience in this field. The complexity of these technologies must factor in as this understanding is vital in determining strategy, company values and culture. A chief data officer should be recognised as an asset within the senior leadership team to ensure accountability within an organisation and with regulators.

Business Management in the era of Big Data

Finance businesses are often complicated, running from a range of systems. Operating normally in this new world of big data will present new challenges that will require time, investment and new resources to help businesses prosper. Despite its significant rise, big data isn’t something for the finance industry. An element of the transition to big data will be focusing on existing data and using this information in new and innovative ways.

As the industry progresses, businesses must show that they have analytical capabilities, appropriate measures and suitable data storage facilities to use big data properly. Without these core elements, they will potentially be at a competitive disadvantage and at possible regulatory risk from generating inaccurate findings that could result in unfair treatment to customers. Having higher volumes of data also poses potential issues of cyberattacks.

Treating all customers fairly

All financial companies must be capable of consistently showing that they implement equal treatment with their customers. As companies evolve with the growth of big data, organisations will have to determine how they interpret fairness and how they intend to keep customers at the core of their business.

If a business relies on historical data, it may generate bias created by how information is collected. This trend can continue and be reinforced in areas where there are gaps in data. This can lead to ethical issues of using data for customer decision making and product design. Customers need to have a clear understanding of the impact of making a selected big data decision against them. Businesses should then notify customers why decisions were made and include the relevant criteria. The customer can then determine whether the information is correct or not and provide corrective support. This type of feedback mechanism offers more transparency to the customer and enhances data accuracy for the business.

Artificial Intelligence requires significant data, building and deploying AI and machine learning systems requires significant data sets. Creating key machine learning algorithms is dependent on large volumes of data. To expand and deepen the results and findings made by the algorithm, machine learning requires data from a range of sources, in various formats and from a variety of business processes.

At the same time, AI itself can be vital in determining and preparing the data required to drive the further value of AI and analytical systems. Businesses require more data scientists and specialised analysts to integrate the necessary AI and machine learning algorithms. 

A new era of enterprise analytics is developing and it involves a combination of automation and contextual information. AI-focused analytical systems can develop vital insights and information that can be passed onto decision-makers without requiring specialist analysts to prepare the data. Business intelligence analysts and other data professionals will still play an important role, but many will not be needed to provide added support to other team members and data users.

Smaller businesses that don’t necessarily have the budget for data scientists will be able to measure their data with better accuracy and clearer insights.

The potential to efficiently automate data tasks is dependant on the industry and overall circumstances. Often, there is a need for adequately trained human support for AI and machine learning plans, especially if the output is critical to the business.

While automated AI data science tools can be simple and effective, they may leave businesses with unanswered questions. If you don’t have a background in data science or machine learning, you may not be capable of determining the results or implementing the suggested changes, which can be challenging and time-consuming.

There is the potential to automate certain parts of a data scientist role, but the skills of a data engineer will continue to be a vital asset to an organisation. Data engineering is required to produce smart and intelligent information that can enhance predictive accuracy and support detailed business analysis.

There may be ways to automate various pieces of data science roles, but the skills category that will still be essential is that of a data engineer. There are many tasks required to source, manage and store data in which data scientists don’t necessarily want to get involved. “To succeed with AI, companies should have an automation environment with reliable historian data,” a McKinsey report explains.

Then, companies “will need to adapt their big data into a form that is amenable to AI, often with far fewer variables and with intelligent, first principles-based feature engineering,” the study’s authors, led by Jay Agarwal, state. Data engineering is needed to produce “smart data” to improve predictive accuracy and aid in root-cause analysis. This, along with equipping staff with the right skills, can provide services that can help increase revenues up to 15 per cent, they relate.

Data engineering is vital. A data scientist can’t discover or utilise information until there is a good set of data to work with. Data scientists and specialised data analysts will continue to be in demand and will remain important in supporting businesses to design and test algorithms and data that can determine trends, automate processes and engage with customers. The challenge, however, is the volume of data flowing into businesses and the rising demands for new algorithms and capabilities with data. AI is unravelling a new path to a more effective and accessible AI.

Financial services businesses within the UK are becoming more reliant on scraping alternative data sources, with over 60% using alternative data to improve their decision-making process. The new report ‘The Growing Importance of Alternative Data in the Finance Industry’ by Oxylabs highlights the significant rise in web scraping for alternative data over the last year or so.

Over 200 senior data decision-makers in the UK finance industry were interviewed on their existing approach to data management. The findings indicated that web scraping and financial transitions were the most popular sources of alternative data for financial services organisations. This includes non-traditional data sources that may not have been assessed before, such as social media posts, website traffic and other data sources. Conventional sources like official public data and third-party data are still considered valuable but have been overtaken by the significant rise in alternative data.

Julius Cerniauskas, the CEO at Oxylabs, explains that the rise in online alternative data sources has created a sharp increase in demand for web scraping services from financial organisations looking to tackle the challenges from the pandemic.

Cerniauskas states that they have experienced a surge in inquiries from businesses in the financial services industry over the last year, so he explains that they are motivated to learn how these organisations were approaching data collection and analysis.

Alternative data can be implemented to gain a better understanding of business performance, market trends and future investment plans. Financial services businesses can transform alternative real-time data into clear, actionable insights that are far more likely to report significant improvements in decision-making.

Business leaders in the finance industry are continuing to explore new ways of improving investment decisions and reducing risk to their business, so it’s understandable to see that the global alternative data industry is growing and predicted to continue increasing over the next few years.

Looking at the research, it’s clear that financial services businesses are increasingly looking to utilise alternative data to gain more value and discover new insights into performance, industry trends and potential investment opportunities. Data-focused organisations will be in a stronger position to convert this valuable information into actionable insights and deliver strategic decisions in a post-pandemic economy.

Open banking is the transfer of financial data and a trend that inevitably is likely to increase. While open banking empowers fintech and larger technology businesses, a report by McKinsey suggests several significant benefits for established banks and financial organisations that may have not been previously recognised.

Industry experts highlight how our world is changing and how important information is to businesses. Customers are not willing to accept a particular service or price if they are aware of better options elsewhere. Several reports from McKinsey suggest that embracing open banking is necessary and has benefits for financial institutions as much as it had for fintech and other businesses. 

The findings indicate a value to the adoption of open financial data, an increase in GDP of between 1% and 1.5% within the U.S, the U.K. and the EU.

While it’s not exactly clear how open financial data will progress, the trend towards data sharing between financial institutions, fintech and other big businesses is only going to increase. This will have a significant impact on the traditional banking industry shortly.

In the report ‘Financial Services Unchained’, McKinsey explains that if open finance continues to accelerate it could transform the global financial services system and change the concept of banking altogether. The report goes onto say that the ability for customers to gain a deeper understanding of their finances could result in margin compression, as charges and pricing becomes more transparent. McKinsey explains that banks may have to compete with margin sharing, as payouts to other digital platforms could play a bigger role in customer acquisition.

McKinsey also highlights that open financial data places big technology businesses in a stronger position to become financial services leaders. We are increasingly seeing more big tech businesses entering financial services, using open data as part of their offerings. It’s worth remembering that multiple businesses are capable of using the same data and as a result, big technology businesses will have banking partners and will continue to face several banking competitors.

Increased competition will ultimately lead to the need to understand and respond to new changes, restructure offerings, adapt business models and establish partnerships with fintech or technology businesses to drive continued success and relevance.

The benefits and value of open data

While it may sound like conventional financial businesses may face a challenging future, the report ‘Financial Data Unbound’ by McKinsey details several benefits of open financial data and specifically relate to financial institutions.

In most cases, financial data sharing is quite limited to areas within financial services, but there are several benefits to customers and small businesses from open finance.

Increased Access to Financial Services: Data sharing allows customers to purchase and use financial services that previously they may not have had access to. For example, open financial data can support the credit assessment of borrowers by measuring utility, phone bills and other factors.

Enhanced User Convenience: Data sharing can save substantial time for customers in their engagement with financial services and, more importantly, for product purchases and exit. For example, open access to data on mortgage products enables customers to apply for loans without engaging a mortgage advisor.

Improved Product Options: Open financial data can provide an enhanced range of options available for customers and create further savings. For example, open data systems make it simpler to switch to different accounts, supporting small business customers to gain the best results.

Benefits of Open Data to Financial Institutions

Fintechs and other third-party services have displayed clear benefits by having the ability to access customer banking data that was previously unavailable in conventional banks. The benefits to other financial organisations aren’t necessarily as clear, but they do exist.

McKinsey explains that the open data systems are progressing in various ways that don’t necessarily translate into a clear win-lose situation for banks and fintech. Some banks will be able to leverage open banking and take a share of this emerging market. The McKinsey report several benefits from open banking for financial institutions:

Enhanced operational efficiency: open financial data could significantly reduce costs by replacing physical documents with verified digital data, making it simpler to adopt automated technologies. This will improve customer experience by enabling quicker and more transparent interactions.

Better Fraud Protection: Improved fraud protection can mean considerable cost reductions for financial businesses and an overall improved customer experience. Sharing fraud-related data creates more evidence and insight to support detecting any suspicious activity.

Improved Workforce Allocation: Financial organisations can use open data to allocate and support their workforce, assigning particular members to high-value activities.

Improve the Data Intermediation Process: Open banking systems create direct access to data via APIs for intermediation, reducing overall friction. Data sharing decreases or eliminates the costs financial organisations experience in data sourcing with third party providers and other aggregators.

Many business leaders believe that traditional practices can only deliver a certain level of results. This way of thinking has influenced financial and investment leaders to combine conventional data sources with other innovative practices. In the pursuit of delivering the highest results and gaining a market advantage in the finance industry, businesses are exploring new and obscure data sources that have actionable insights.

Alternative data refers to non-traditional data sources that finance and investment firms use to measure and guide their strategies. Examples of alternative data range from ESG information, credit card transaction to satellite imagery and weather data.
The number of alternative-data providers has grown to 20 times the size it was 30 years ago, with studies suggesting over 400 active providers on the market, compared to only 20 back in 1990.

Today, approximately half of all financial investment firms use alternative data and this number will likely continue growing as more businesses invest in new technology during and preceding the impacts of the pandemic. New data sources offer unique advantages and the opportunity to reveal new information that can differentiate a business from its competitors. Alternative data providers have increased considerably in recent years, but access to new data sources doesn’t necessarily mean an added advantage.

Comparing raw and aggregated data

Alternative data usually comes as aggregated data sets or as a straight data feed via APIs. Aggregated data is regarded as the more affordable option and is structured and easier to work with. However, these sets are more common, and because of that, they have less potential for alternative data structures. Alternative data specialists explain that this form often lacks depth, and businesses can lose the ability to explore their data in more unique ways.

The challenge today is how sure are we that a data set is creating value? It’s difficult to determine how much impact information is going to have until much later. Even with well-structured feeds and benchmarked data sets, the requirement for a skilled data analyst in the finance industry is only going to continue rising.

As discussed before, big data adoption is nothing new, and many businesses are currently utilising big data in some shape or form. However, alternative data has only really taken off in the last few years. Given the current level of adoption and the fact that over half of investment managers are now leveraging this form of data, some believe that alternative data could become more mainstream. Creating a data advantage, even it may be small can create a big difference in today’s competitive marketplace and support leaders with making quick and important decisions.

While hedge funds were one of the early adopters of alternative data, multiple industries now apply this form of data. Alternative data is openly available, and in many scenarios, it is free for all businesses. When executed and aggregated properly, alternative data creates powerful insights that were not possible in previous years.

The potential of alternative data is something more businesses are beginning to understand, and a growing number are taking advantage of this innovative data source. As it becomes more mainstream, it is likely to be adopted by more companies very soon.

There have been very few technological innovations that have impacted the finance industry, like Big Data. The traditional days of customers visiting local banks have been replaced with a wide range of diverse online products, as well as some use of in-branch services.

The banking industry experienced a significant transformation with the emerging digital changes. Abundant data sources are now available to financial businesses, enabling companies to gain a better understanding of their customers and create a more personalised service.
As more structured and unstructured data is generated by customers through loan applications, credit limits or online transactions, Big Data analytical tools are being utilised to create clear actionable insights. As an example, the Bank of America was one financial organisation that applied social media data to determine service issues with their customers that impact overall customer retention. When the bank used big data to assess thousands of comments on social media, they discovered lots of misinformation regarding purchase limits which potentially impacted their customer attraction and retention. Being capable of discovering customer issues quickly before they grow further is a powerful tool that big data technology can provide.

Businesses in the finance industry use several big data technologies such as artificial intelligence, machine learning and natural language processing. In a continuously increasing competitive market, businesses need to integrate innovative technology to gain a more competitive edge. A survey by Capgemini suggested that over 60% of financial businesses believe that Big Data analytics provides a significant competitive advantage and over 90% believe that successful big data measures will determine the leaders of the future.

The restrictions implemented from the pandemic have placed more emphasis on the digital services offered and available to their customers. While the transition to digital is nothing new, the impacts of the last year have accelerated this movement to new and innovative services. As physical branches reduced their hours or temporarily closed, many financial services have moved online. Without the support of big data tools, banks have become overwhelmed by the high volume of new applications and enquiries. Customers that experience delays or waiting times could potentially move to an alternative bank that offers better customer service.

Banks need to remain focused on assessing all factors before offering credit to a customer or approving a loan. Using relevant customer data with big data technologies improves this process and enhances overall risk management. The more data credit risk management solutions available, the more accurate the credit scoring will be.

The transition and rise of digital have brought a higher incidence of fraud as many face-to-face transactions have been replaced with online services. HSBC uses machine learning and AI to explore potential fraud in various ways by checking IP addresses and monitoring irregular transactions. But customer service remains the top priority for deploying big data technologies. During the pandemic, the bank experienced a significant rise in customer enquiries, and chatbots became vital communication tools. Using Natural Language Processing technology, chatbots can convert text and connect it to established patterns to deliver relevant answers. The text is fed through machine learning tools to determine concerns or challenges faced by their customers.

Standard Chartered Bank uses big data to gain more insights into customer behaviour and target them with specialised services and deals. With real-time data and analytics, valuable information is generated from regular transactions.

As the Economist declared a few years ago, the world’s most valuable resource is no longer oil but data. There is a definitive need for financial businesses to embrace the benefits of big data moving forward. The global market for big data analytics is forecast to increase by an annual rate of over 22% until 2026. Financial businesses are more aware of the necessity of integrated big data tools into certain areas of their business. Big data tools are continuing to influence the financial landscape and support customers issues, increase retention rates and reveal specific insights about customer behaviour.

Across the entire business, the predominant focus lies in implementing data-driven decision-making processes. Applying the potential of technology, advanced analytics and data, combined with human power to generate smarter decisions.

Despite the benefits, many businesses fail to deliver what they regard as a measurable value from their data. A study by Accenture recently discovered that only just over 30% of businesses report tangible and clear value from their data. 

Researchers from the MIT Sloan Management Review explained that many businesses tend to focus on finding a purpose for data available or extracting information from the information they have to hand. This results in creating answers to the wrong questions or possible misleading insights. A more effective approach to take is applying decision-driven data analytics. This involves emphasising the decision that needs to be made and then working backwards to explore what data will be best to deliver for that particular goal.

Data-driven decisions often place too much emphasis on the data but fail to consider how the information is generated and result in individuals reaching inaccurate conclusions. The second problem encountered is that people tend to ask the wrong questions. 

Key Considerations

Don’t wander off with your data. Creating a plan and initiative based on the data available can lead to focusing on the wrong questions and reinforce certain feelings within a business. Staying focused on the available, generally historical data will often result in decision-makers taking the wrong path and ultimately gaining insights into areas that don’t focus on the fundamental challenges a business may be facing.

Assign the right people. Most data plans are managed and operated by data scientists, who have a solid understanding of data processes but they may not be ideal at understanding the core problems of a business. Ensure data and analytics plans include other people who have a strong understanding of data and a familiarity with the business goals to enable an effective combination of the two.

Create a plan and succeed. Decision-makers in a business need to determine the right course of action for each challenge they face. They should work out what information is required to assess all possible options and use the resulting analysis to choose the best course of action.

Analytics and big data have proven to be valuable in supporting business insights, but people need to remain in control of the shaping of their business plans.

Businesses today are consumed by so much data, some face the challenge of accessing the information or lack the right tools to analyse their data.

The digital transformation of the finance industry has been happening for several years now. The potential to automate manual processes has enabled finance to transition from its conventional reporting processes to a more innovative forecasting and analytical model. While many businesses confirm they are applying analytics to their organisation in some shape or form, only 14% of finance businesses use large volumes of data available to generate valuable insights, according to the FSN Future of Analytics in Finance report.

Finance teams that have taken this approach to harness this information are better placed to forecast more accurately, create valuable scenarios and explore clear insights that support enhanced decision-making. The FSN report also suggested that 86% of analytics resources were not achieving the mark. The study believes that one of the main reasons for this is that many businesses are not utilising the value and insights from their data.

The survey found that ultimately it is the data that is holding many businesses back. Organisations are either overwhelmed by the sheer volume of data or are held back by the technology they are using to measure their information. According to the report, only 12% stated that they are suitably equipped to manage their data and have all the necessary resources to deliver clear, actionable insights.

The accelerated rise of new technologies available across the market has left many financial businesses struggling to maintain pace. This includes predictive analytics, artificial intelligence, machine learning, robotic process automation and more. While all of these technologies are valuable, creating success requires a holistic-based approach, and many still seem to be working towards this.

The survey indicated that over half of respondents were not capable of regularly adding new data sources to enhance business insights, and under half can make full use of non-financial data. When asked about the key features for analytical tools, many respondents placed AI and ML as top priorities, while at the bottom are some of the most important building blocks for creating an effective analytic system, including the ability to integrate multiple data sources.

The survey findings resonate with developments in the market. Many larger organisations struggle to deliver efficiency and agility in their reports, planning, budgeting and forecasting processes. This is often down to an over-reliance on spreadsheets and manual processes or using fragmented applications that a business may have outgrown or not capable of managing.

Innovative businesses are improving analytical insights by combining processes to deliver a singular system for financial results, budgets and forecasts. Businesses are taking further steps to integrate data processes into their analytics platforms on a more consistent basis. Accessing this type of information and combining it with financial data provides these businesses with clear views into key trends and indicators that enable decisions that can impact the future of a business.

Generating unified and efficient reporting combined with operational data requires the appropriate analytical systems and appropriately skilled talent. In our rapidly developing economy, having the necessary information systems capable of generating clear insights and analytics is not an option anymore but a vital part of surviving and succeeding for the future.