Industry leaders are starting to understand the role that ESG has on how businesses operate; they are looking for solutions that help them manage their ESG analytics. Environmental factors have a significant influence on our quality of life as well as considerable financial implications.
The cost of climate-associated disasters exceeded $650 billion between 2016 and 2018. This number is forecast to increase further, especially when factoring in the impacts of the pandemic on the global economy. Due to the financial impact environmental issues have, more governments and businesses focus on meeting ESG standards.
In finance, businesses should be including various ESG metrics like ESG market analytics, risk assessment, compliance and portfolio management to manage the investment process. Meeting the different ESG standards is complex. ESG-focused procedures require accurate and updated ESG information, which is challenging to obtain due to fragmented, inaccessible and scarce data availability.
Businesses like tech startup Viridian are trying to mitigate these hurdles by creating a centralised information platform for the ESG market. Viridian utilises big-data analytics, real-time monitoring and AI-driven alerts.
Viridian use advanced technologies to one of the most pressing challenges facing humanity: the environmental crisis. Technology and data represent a vital element in tackling climate and environmental challenges.
Many industry leaders recognise the risks we potentially face in the future, whether it be physical, financial, through business or new regulations. Today’s finance industry influences many industries and plays a vital role in the environmental movement.
In the finance world, climate and environmental risks convert into material business and financial implications, raising concerns for finance companies worldwide. The finance industry needs to adapt rapidly and incorporate environmental factors within many financial processes. Various climate factors, market trends and environmental performances have a significant influence on investments.
ESG needs to be incorporated into various financial processes to avoid any disruption and to remain competitive. The challenge that many financial businesses are experiencing is the lack of ESG and climate-related data needed for effective analysis and assessment. Obtaining this data is particularly complex due to the fragmented and inconsistent nature of ESG data. Data analysis is how new tech startups like Viridian can play an important role.
Applying innovative big data technologies, combined with advanced analytics and AI systems, businesses gather the most accurate and valuable information from multiple data sources and present the data clearly and insightfully. Viridian provides a knowledge graph that effectively bridges the gap between customers in finance or government to the array of relevant ESG data, which is vital for the new economy.
Enabling simple access to ESG data, emissions, pollution and compliance to evolving regulations is very important to the finance industry. Viridian provides a flexible analytics service, enabling consistent changes in data, which is ideal for sustainable investment and climate risk assessment since both continue to evolve.
Utilising wide-scale data collection capabilities provides businesses with an accessible platform that is particularly useful for the complex areas of the ESG and environmental economy sectors.
Climate change is becoming a growing concern, and more people are taking more responsibility to make changes. Businesses like Viridian are making their progress by providing a platform that will support businesses, governments make a conscious decision on the importance of ESG.
As the finance industry significantly transforms through the rise of digital and technologies like blockchain become more established, the shift of data will inevitably present opportunities to cybercriminals. Studies suggest that banking and financial organisations are nearly 300 times more at risk of cyber-attacks compared to other businesses.
With the UK financial regulator raising concerns of potential threats of new cyber-related attacks, the industry needs to be prepared and protect itself against an increasing variety of new threats. The conventional security tools are not enough, and businesses need to implement an intelligent stance towards cybersecurity on effective management of detection and response.
Cybercriminals understand that finance businesses contain a large amount of confidential customer data. The industry is expansive and connected to other industries, making it even more appealing to potential cyber-attacks. Individuals are becoming increasingly more sophisticated in discovering and targeting particular areas within the finance market. The rise of remote working has created further challenges when monitoring potential attacks.
The pandemic has accelerated the rise of digital across the UK. The finance industry recognises the importance of investing in new technology to meet customer demands, improve operational response and manage potential risks. This broadening process, however, creates more security challenges for the finance industry. Despite the progression in digital, many banks continue to rely on traditional systems that fail to address the requirements needed for effective risk and compliance management.
Traditionally, business leaders have viewed digital progression and cyber security as separate entities with varied objectives and goals. This siloed approach can often result in finance businesses overlooking potential security weaknesses that have emerged due to accelerated technology changes. Businesses that integrate new systems and upgrades without the necessary security in place are potentially at risk. Maintaining a clear mindset throughout the digital transition is a vital part of developing and maintaining resilience to possible cyber attacks.
Finance businesses need to explore ways to reduce risks without incurring costs. The focus of cybersecurity should shift from prevention to detection, containment and response. The Cost of a Data Breach Report by IBM explains the importance of detecting how and when a cyberattack has happened and how to respond appropriately. Businesses that take a short term preventative approach and don’t invest in future strategies are often the ones that take longer to recognise that a cyberattack has occurred.
Combining AI, automation and human analysis enable enhanced visibility over particular systems, allowing businesses to detect and prevent cyberattacks. The methods and reasons for cyber attacks will continue to evolve, so the finance industry needs to be one step ahead without impacting the digital capabilities that individuals demand. The best way to improve cyber resilience is by creating a cyber security strategy based around Managed Detection and Response (MDR). Success relies on ensuring businesses have the appropriate processes and people in place to manage new technologies. With many businesses lacking the necessary security talent and capabilities required for operating an efficient MDR, working with a separate security specialist will be critical.
By collaborating with a trusted team of experts, businesses can benefit from an agile solution that builds customer confidence and secures data. In today’s continuously evolving cyber landscape, it will be the businesses that apply a proactive approach towards security management and implement a cyber security process that generates the benefits of a more solid and structured IT system.
Finance leaders have had to make critical business decisions faster than ever before, causing an accelerated focus on the importance of big data and predictive analytics. Implementing strategic decisions has become essential for finance leaders while businesses transform their finance systems and adapt plans to continue growing. With this rising pressure, predictive analytics has become an even more vital tool in supporting the current challenges and providing a competitive edge over other businesses.
Businesses recognise that the more use they make of their data in developing plans and building scenarios, the more they can be ready for future disruption and changes. A blend of predictive analysis and simulations will enable clear insights and scenario development. This process is becoming more necessary as unpredictable events, ranging from climate change to geopolitical, continue to disrupt business plans and all need to be accounted for.
Predictive analytics today plays a distinctive role in strategic and operational decision making, with many industry professionals stating that a decision without data input has little or no value. A report by Deloitte suggests that just under 50% of senior executives believe that the main benefit of applying analytics is its influence in driving better decisions.
The responsibility of a CFO has become more focused on balancing resource management and enabling the continued growth of other strategies that may lie outside of standard operations. If a business has access to data, they will likely want to generate insights on what is happening and determine why these activities are happening. Companies want to harness this information and apply predictive models to gain a better idea of future trends.
The pandemic created a seismic shift in the role of the CFO and their importance in business strategy and responsibility in business model transformation. Generally, any transition period involves a drop in initial revenue, followed by a long term plan to improve this. The more analytical support available, the better strategic and operational plans a business can make to reduce the impact of this change.
Predictive analytics is generally associated with supply chains, marketing and HR. In finance, there are many opportunities to improve and automate financial reports. CFOs can utilise automation and predictive systems. They recognise that it is necessary to enhance resources and apply new technology. Those finance leaders that have taken on predictive analytics into particular areas such as cash, audit, FP&A are likely to have a competitive edge over other businesses. However, it isn’t a simple transition and will require upskilling of employees to gain the benefits of this transition in finance.
Upskilling finance professionals and delivering an environment where knowledge is shared is critical. It will reduce time spent on particular activities and strengthen the relations across the business.
Automation analytics will create a competitive advantage for businesses as it creates additional time and resources that work towards high-value activities. With the correct insights and processes, the overall time to value is reduced considerably. It allows for more time to focus on value-added activities and to establish new opportunities within a business. Companies today are working with a range of data sources which means that it is even more vital to have the necessary tools to analyse this information.
Establishing a clear data plan that can effectively combine different sets of information and make it available to decision-makers with the right insights is probably one of the biggest challenges businesses face right now. When a data plan comes together, it can deliver significant benefits to businesses and create a distinct competitive advantage over other companies.
How storytelling can create deeper connections and allow your audience to relate to critical financial data
In the world today, finance professionals have greater access to a range of data and information. More information, however, may not necessarily translate into meaningful and decisive action. What is vital for people in finance is deciphering how these numbers affect their role and the business.
Factual information, numbers and lots of data can be challenging to understand. If information is displayed more interestingly and humanely, people are more likely to connect, remember and take action. Storytelling is one of the most impactful methods that has emerged over time to connect and share information with others.
Becoming an efficient storyteller may not necessarily have to be such a challenge. It does, however, require time to discover a story that relates to your data and then determine the most appropriate way of presenting the information to your audience. One method of reducing time spent on this process is utilising automation, allowing finance professionals to focus on discovering and delivering key insights.
The concept of storytelling has grown in popularity, as more businesses adopt automation and recognise the impact it can have on finance professionals. Storytelling is establishing itself as a vital tool within finance, creating deeper connections within an organisation and building the required company culture.
Storytelling is often considered as something that requires a sophisticated narrative, but it doesn’t have to be this complicated. The key is creating an accurate and logical way of sharing insights and ideas. The concept of storytelling is predominantly focused on improving communication. The important part is ensuring you highlight what discoveries you have found with the data, particularly information that is important to your business or a client. If this step is forgotten then the rest of the process is redundant.
After determining the key data points, the focus is on presenting the main findings and delivering the core theme. Considering the problems and how this impacts your audience, followed by a summary of insights and possible solutions is an efficient way of developing your story.
When converting detailed, data-focused financial details that many will not understand, you are effectively transforming numbers into something that is relatable and allows individuals to recognise the importance of the data. Visuals are often a useful complementary part of a story, displaying a visual representation of the information. Visuals, however, should support a story, and not tell the story on their own.
A business needs to understand its audience and its language when putting together a report or presentation. Considering what’s important to your users, what motivates them and ensuring they recognise the insights is critical.
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.