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.
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.
CFOs and other financial professionals are fairly optimistic that we are approaching an economic recovery. Over 70% anticipate that we will return to normal by the end of the year, according to the latest Enterprise Financial Decision-Making report from OneStream, a leading provider of corporate performance management solutions. The report suggests that businesses have increased their investment into data analysis tools and general usage considerably over the last year.
The study by OneStream explored financial leaders across North America and determined certain factors that influenced their priorities, budgets and technology integration plans for this year. The findings suggested that the pandemic had generated a bigger need for flexible forecasting, predictive planning and digital transformation. The capability to forecast budgets quickly and change workflows have become a critical feature since the pandemic began.
The report also discovered that financial executives have significantly increased their usage and investment in data analysis tools. Businesses generally tend to invest in artificial intelligence and the results showed increased use of cloud-based planning and reporting solutions. Most businesses (around 70%) are already using or plan to adopt (20%) a low-code development platform that allows business users and developers to adopt new roles while managing complex coding requirements.
In the case of return-to-office budgets, data privacy tools are regarded as the most common priority, followed by hybrid cloud solutions.
In comparison, the OneStream 2020 Enterprise Financial Decision-Making Report indicated that less than half of finance executives used cloud-based solutions regularly for reporting. The 2020 report also showed that less than a quarter used machine learning and artificial intelligence solutions.
Many financial leaders are exploring their workforce, technology and supply chain requirements in a post-pandemic world. However, the influence of changing policies and social development has had a significant impact on investment plans, encouraging leaders to focus on sustainability and diversity measures as well.
The study was conducted by Hanover Research this year covering hundreds of financial decision-makers in Northern America. The full OneStream Report – Enterprise Financial Decision-Making 2021 – North America can be viewed here.
Businesses today are becoming more and more reliant on data and fortunately, the availability of data is rising considerably. The amount of information generated by one business would have simply overwhelmed some of the largest companies from a decade or so ago.
Yet, the majority of people in recent studies confirm they continue to find the volume of data too much at a certain time, while another 45% from the recent State of Growth report stated that the lack of data represents one of the biggest challenges in business.
How can the finance industry work through this balancing act of remaining in control of their data and satisfied with the volume and quality of information they have available?
Enterprise Resource Planning (ERP) systems have become a vital tool for the management and organisation of business data. While each team may only require access to a certain portion of the data, combining it all within one system creates a much clear and effective view of the entire business. There are certain considerations to think about for financial analytics and successful reporting.
Are the tools you use suitable for your business?
Spreadsheets are popular reporting options due to their simplicity but they have their limitations. Working with conventional spreadsheets can result in errors, often due to calculating formulas incorrectly. To create a higher level of accuracy, information needs to be constantly updated, which makes it complicated to maintain this level of data quality.
How accessible is your data?
Combining various data sets in different ways is one of the biggest benefits of applying the right tools to your business. This could be combining financial, production and other data to determine the production costs of a selected product in two varying locations. This level of data wouldn’t be achievable if the business is using varied systems or spreadsheets, or possibly the incorrect ERP system. Utilising an ERP system with a singular database makes it simpler to create centralized access to the information across the business. If data is being captured in this manner, it becomes easily available for various teams to assess and generate new insights.
Are the reports flexible?
ERP and finance software providers generally provide a range of pre-formatted reporting templates that can be applied to standard reports. This reduces time and money spent by eliminating the need to generate frequently used reports. The problem is that one format won’t necessarily work for another. Using predefined reports can be useful but many financial reports need to be customised to present data in other ways for different clients. Often, altering the layout or incorporating new data into an existing report can be complicated and time-consuming.
Are there restrictions with your KPI monitoring?
KPIs allow business leaders to understand how well their team is performing against certain objectives and enable businesses to focus on reaching specific targets. Most software solutions are embedded with built-in KPIs but to be effective these KPIs must correspond with the correct business model. No two businesses are the same and some software providers do not appreciate this, assuming that several standard KPIs are all that business requires.
Can dashboards be customised?
Associated with the KPIs are dashboards, which are an important element in providing visibility into goals for a business. In addition to displaying KPIs, they can support individuals to remain focused on their work, keeping them alert to potential issues that need to be addressed or certain jobs that need completing.
To grasp the full potential of data, businesses need analytics that truly add value to your business and reporting that is insightful. While many software providers offer various tools, not all systems are equal. Selecting the incorrect solution that has irrelevant data can limit the ability of a business to take full advantage of its information. Selecting the best solution will generate a higher level of flexibility and confidence that the right information is being channelled to the appropriate people at the right time.
Allen Shim became the CFO of Slack after shifting his attention towards what priorities the position needed.
Back in 2014, Slack had around 20 employees when Shim joined as a finance executive. He quickly became responsible for managing finance, analytics, accounting, as well as other functions within HR, IT and legal.
In a previous interview, Shim explained that he felt like he had the responsibility of a CFO but it wasn’t until he stopped wanting the job that he began to understand how he had been limiting himself through certain actions.
After experiencing a complete review in 2017 one thing stood out to Shim and that was that his fixation on becoming a CFO was hindering his overall effectiveness. Taking this on board, Shim reconsidered his performance and understood that rather than focusing on his achievements, it was more important to determine whether he was the one capable of driving this business forward. It was this shift in mindset and how he interpreted his personal development over time that was so critical.
After further discussions with other CEOs, Shim reassessed his organisation and quickly realised that he was overseeing multiple functions. As a consequence, Shim was spreading himself too thin and incapable of allocating enough time to each task required to be an effective CFO.
Shim highlights that to become a CFO, he was required to become excellent in financial planning and analysis (FPA) and appreciate exactly what factors would drive the business forward. Shim started focusing more on the way a CFO could create a structure in the business, supporting scale and growth over time.
The shift in mindset enabled him to apply more energy towards business strategy and ironically ended up with him being offered the role of the CFO later that year. The change in focus on career advancement ultimately enabled him to progress in a way that facilitated the goals of becoming a CFO.
The last year came with several uncertainties, exposing vulnerabilities in society and transforming how we do business. With the disruption to face-to-face services, the finance industry was forced to explore alternative, innovative ways to reach out and connect with customers.
Shifting from face-to-face services to alternative plans isn’t easy. Apart from the potential risk of losing existing customers, there are the added worries of complying with certain procedures and ensuring the service offered works for your users. With this in mind, several key challenges are facing the financial technology industry:
The shift in finance – the traditional finance industry has shifted dramatically as a consequence of the pandemic. In 2020, over 3,300 physical banks closed their doors in the United States. At the same time, we have witnessed a surge in challenger banks. At the beginning of 2020, only 4% of millennials and Gen Zs were prepared to use a challenger bank account as a primary one. By the end of 2020, this figure increased to 15%.
For financial providers, this means they will need to adapt and be prepared for a more diverse audience. Gone are the days of providing a one-size-fits-all solution. Instead, industry demand suggests that customers require bespoke services. One area to focus on is utilising technology solutions like big data and automation to ensure the services meet customer demands.
As the conventional banking industry changes, emerging industries like cryptocurrency are transforming too. Earlier this year witnessed a significant rise in bitcoin prices and other altcoins. The crypto industry is closely affiliated with blockchain technology and it is in this area where we may witness more changes. From smart contracts to trades, blockchain can offer security to traditional financial services and alternative markets. Reports suggest that by 2030, blockchain will impact global GDP in big ways.
At the same time regulators worldwide are focusing on new legislative solutions to make the financial industry more secure and create legislation for anti-money laundering in regards to blockchain assets. For financial solutions providers, this is the time to explore how services and technologies like this impact their industry and how to work with them in the future.
The Lending industry
At the beginning of the pandemic, many people experienced a financially unstable situation due to loss of employment, furlough or health-related issues. With a continued rise in online services and restrictions easing across the world, there has been an impact on the lending market. For customers, this could involve a shift from desire-focused lending to more needs-focused lending. This shift is associated with a decline in credit card searches and an increase in purchase finance and commercial lending, indicating borrowers are becoming more cautious and selective in their choosing loans and providers.
For traditional and alternative providers, a similar lesson resonates. It is important to consider the changing needs within the consumer market. Whether this translates into creating a smarter, more customer option or transferring to a digital platform, each service will vary. Whichever service you choose, it is essential to find one that works for your clients.
Today, the finance industry is in a transformation stage. Many traditional processes have been proven not to be as efficient or as reliable as previously thought, and the concept of how finance and lending have been changed significantly. The industry is still reshaping for the future and while it still isn’t completely clear what shape the industry will take, it is likely to be focused largely on data, new technology and prioritising the needs of customers.