As volumes of data continue to increase, natural language processing is emerging as an important element of financial analysis. Businesses are continuing to embrace machine learning to enable quicker and more efficiently informed decisions. Financial analytical businesses are shifting their attention towards natural language processing to analyse data significantly faster and more accurately than possible by humans. 

Many assume that financial data is largely numerical rather than textual but industry experts suggest data that enables timely decisions generally comes in text. Text is unstructured data and is typically more complicated to use, which is where natural language processing can play an important role. A form of machine learning, NLP can parse complex elements of audio corresponding to business and finance, including phrases, numbers, currencies and product names.

For example, earnings reports are one method that is released as a text. Extensive time is required to transform this information into structured data. NLP can generate transcriptions in just a matter of minutes, providing analysts with a competitive advantage.

NLP may be relatively new to the finance world, but as it continues to accelerate, the industry can utilise the years of research and development from other technology leaders, including Google and Facebook. These types of businesses have worked with NLP for several years and can provide a clear path for the finance industry.

Whether your business is researching a company or exploring data sets on a particular region that is beyond capable of a human doing, businesses will rely on these types of technology even more. There are several ways in which NLP can improve decision making and enhance the response time of financial businesses:

Automation: NLP can replace certain manual tasks and convert unstructured data into a more effective and usable form. For example, this can include, management presentations or acquisition announcements.

Enhancing Data: Once unstructured data is collected, applying context can make the information both searchable and actionable. Machine Learning can enrich raw information, identify particular sections that may have a financial impact or other particular areas of concern to the business.

Improve Search and Discovery: The finance industry is actively seeking to find a competitive advantage in terms of data variation. However, what is important is delivering a search experience that is as efficient and effective as the google search bar that customers are used to. It can be very challenging searching data at a bank or hedge fund. Financial analysts emphasise the need for intelligent systems capable of understanding the industry.

For financial businesses looking to gain the benefits from these technology services, the barriers to enter the market are significantly lower than in previous years, due to technology being more affordable and easier to implement. With the advancements in technology today, it’s realistic to implement innovative NLP in finance without having the skills or experience in machine learning.

The competition between major technology businesses has enhanced the machine learning environment for interested stakeholders. Technology leaders are investing significant money into creating efficient machine language systems and in pursuing market dominance, have generated available frameworks for other businesses. 

Businesses are still trying to determine the most effective way of implementing machine learning and for the finance industry, there isn’t necessarily a single solution. Companies could create machine learning products or build their data science team. Machine learning can be applied pretty much anywhere, from a low-level data collection point to a high-level client-facing service.

The recognition of having valuable data that isn’t being fully utilised is the general drive for implementing machine learning. Businesses understand they have all this data that is too much for humans to use, so how can machine learning and natural language processing be applied? For financial businesses, which often are cautious with adding new technologies like machine learning, this understanding is critical. As more people see the products and understand the processes, they begin to realise how it works and values it offers. 

Information can deliver vital insight and important benefits for small businesses and performance management tools can enable leaders to understand more about their employees.

Understanding what your employees are capable of performing is a critical aspect of managing and operating an efficient workplace. Daily observations do provide some insight for senior managers but it doesn’t necessarily deliver a complete picture. Performance management software can identify valuable data insight to generate a better understanding of each employee’s strengths and weaknesses. If implemented correctly, performance management software can enable a lot of additional value for a business.

What are the benefits of performance management software?

In most HR teams, the drive for clear, decisive data is particularly important in regards to talent management. Today, HR teams utilise performance management software to assist in simplifying several processes, including appraisal and goal developments.

Improving efficient HR

Organisation in HR is a critical element and employee performance management tools enable HR teams, managers and employees to gain a clear understanding of their business. Gathering key data sources, performance management software can eliminate some of the challenges facing HR teams. It can enable efficient data analysis, allowing for enhanced data collection and generate better and more constructive employee feedback.

Knowing what areas an employee is excelling in and what needs further improvement is significant for employees. By gaining a clear understanding of where an individual is excelling and what areas may require further work, employees can be driven to improve and do better. Performance software can deliver real-time information for employees on a more structured, consistent and granular basis.

Clear insight into an employees strength and weaknesses

Every employee is different, some may excel in certain areas, while other individuals may not meet certain company standards. Being capable of assessing individual performance data, senior managers can discover areas where individuals are exceeding expectations and position them in roles that leverage their stronger assets. In contrast, the data can identify particular weaknesses and support individuals with how to improve these areas.

Reward performance levels

Performance management tools support managers in identifying employee goals and aligning them with wider business goals and values. When assessing employee rewards, performance management systems enable a clear understanding of when and how to reward particular employees. 

With the transition towards remote working, businesses believe that performance management software has become even more important and beneficial for smaller businesses. More businesses are relying on performance management tools to enable their businesses to remain on track. Performance management software supports employees with all functions, impacting the individual, the team and the wider business.

Every business has areas that require further focus and improvement. HR teams and managers must be capable of identifying potential problems before issues evolve and impact further on a business. Being able to evaluate particular areas of a business, and employees that may be hindering business success is a critical process right now.

Employee management software enables managers to determine how well employees are performing their jobs. A range of data sets is utilised to understand the impact and effectiveness of each individual and their impact on the values and goals of the business.

The recent economic challenges have accelerated the need for businesses to focus more on supply chain management, and in many cases, this requires added support from banks. Vikram Gupta, the head of the banking product engineering group at Oracle Financial Services explains that access to capital can be a challenge, and the pandemic has only highlighted the issues of transactional risks further. Gupta highlights that nearly every industry has experienced some level of impact and many small businesses have found it particularly challenging for working capital due to low demand, extended payment terms and increasing inventory levels. The innovation and efficiency from digitalisation play an important role in supporting a business-enhancing working capital, but banks can also play an important role. Gupta explains that banks can provide an alternative, low cost and less document-heavy form of financing, like supply chain financing. Gupta emphasises that funding can even be applied to vendors or dealers, and the corporate’s balance sheets.

Based on these details, Oracle Financial is offering a new supply chain finance service to support banks in delivering more flexible financing options. Providing early funding to supplier corporates or extended payment terms are other ways to support working capital plans. Banks are in a unique position, with details covering every payment going to their corporate client’s account. Understanding when invoices are raised and where capital is required is the area that should be focused on a growth and partnership model. By leveraging balance sheets, smaller corporates will have more affordable and quicker access to working capital, which in turn enables banks to generate added revenue streams from interest and fees.

Supply Chain Financing

Gupta explains that there are several important factors to consider in terms of automation with SCF. This includes managing physical documents and converting them into specific inputs for a business process and automating business processes for quicker turnaround times. To do this, a range of innovative technology is required, including natural language processing (NLP) which converts documents into on-screen information and AI-focused services to reduce human error. Having real-time details of information for the end customer and the bank’s operational users enable everyone to use the same information and provide clarity on the status and predictability of cash flow. 

Identifying unmatched payments continues to be the biggest challenge in supply chain finance right now, Oracle’s focus on automating and streamlining this issue has made tracking simpler. The oracle platform can identify the groups associated with the payment and match them with outstanding invoices and finances in real-time, while at the same time, provide instant status updates to corporates and banks.

Reducing Disruption

Any software changes on a bigger scale have to be capable of connecting with existing systems used by businesses and Gupta explains that Oracle can do this with little disruption. Oracle’s experience with banks enables it to deliver solutions that can be easily integrated and transformed within a business.

Gupta highlights that their strategy is to create an API for everything, meaning the applications are open to receive and provide information from legacy systems in a bank’s system. 

Capital Access

Whether it be poor borrowing costs or lack of strength in balance sheets, capital access has always been challenging for smaller businesses. Smaller companies also face further challenges due to changes in account payables and account receivables. The speed at which you manage funds can determine the winners in this industry, according to Gupta. Gupta highlights that generating money churn through the business is how any business generates more money. Based on the current climate, the need to streamline activities and reduce complex processes is critical. Gupta highlights that in the current conditions, every bit of money that can be collected and any government funding that can be utilised will have an impact.

Predictive analytics enhance financial processes by providing key insights into potential problems in a business and possible opportunities.

The pandemic has created a significant period of uncertainty, with many businesses being forced to rethink their strategies. The finance leader, along with the CEO will be the leading force in navigating a business through uncertain times and ensuring the company can continue with its plans. This is a challenging task and will require supportive information in the form of predictive analytics to determine the right path for the future.

The variations in time spent analysing data compared to conventional methods of number crunching greatly differ between nations. A study by Sage indicated that 50% of financial managers are spending more time on innovative data analysis. In contrast, nations such as South Africa, the figure stands at 64%. The report ‘CFO 3.0: Digital transformation beyond financial management” explored how predictive analytics technology could change how finance leaders operate at board level.

Where to begin

For businesses not applying predictive analytics, a good place to begin is assessing the existing state of financial systems and processes within the business. For example, has the business automated processes for reporting and generating financial information? Does the business have a modern integrated solution in place that enables quick access to financial information?

If the answer to this is now, then the finance leader should consider shifting away from inflexible legacy systems and manual platforms to adaptive, cloud-based solutions that provide real-time information and insights.

Providing finance teams with skills and experience

Finance departments and CFOs may need to utilise the skills and expertise outside of the finance market to enable the opportunities available within analytics. 

Prioritising automation for your business

Finance teams can enhance their analytics processes by applying a focus on automated data analytics. Automation provides an effective way of improving the quality of financial data by streamlining data preparation and aggregation.

Developing a culture of automation can enable higher productivity by reducing manual processes and potential errors, and speeding up processing times. Automation can enable faster decision-making processes while also improving regulatory compliance and improving the accuracy of financial information. Technology today can automate a number of standard reporting systems, as well as the development of dashboards. 

Enhancing processing

As business operations continue to change, finance teams can leverage data and analytics to enhance engagement with other businesses and manage overall performance. Finance leaders should focus on ensuring real-time data and analytics are available within stages of operational decisions, speeding up processes and reducing costs via automation.

The study by Sage makes it quite clear that where businesses lack any real cloud-based financial management tools, there is a general lack of strategic decisions in place. In contrast, predictive analytics creates an effective platform for finance processes, providing insights into potential problems in a business, as well as opportunities on offer. There are a number of ways finance leaders are utilising predictive analytics. This includes:

-Predicting revenue: marketing, sales, operation and user behaviour data enable finance teams to forecast revenue streams more accurately and predict future demand for particular products and services.

-Finance leaders are using predictive analytics to enhance efficiency in a number of creative ways, including ranking vendors in terms of fraud vulnerability, to assessing potential equipment failures.

-Financial leaders are using predictive analytics to assess potential trouble areas that may be reducing company revenue. For example, businesses can use predictive analytical modelling to measure indicators of customer loyalty, enabling action plans to identify issues and retain them.

-Fraud detection is something that many finance leaders are very keen to utilise by implementing analytics to discover and detect potential issues of fraud.

As the finance industry realises the potential of data analytics, investment in business intelligence tools continues to rise. One challenge is ensuring analysts are provided with enough time to utilise the full potential of these services and not bombarded with other business-related activities. 

A lack of structure can lead to a disjointed approach to data management and result in inefficient use of time, money and resources. Businesses need to ensure they maximise their investments into business intelligence tools. 

Nearly every company has some version of a business intelligence solution, however, many analysts spend a large portion of their time on data preparation and other activities. In finance, businesses depend on accurate data to deliver reliable and intelligent business decisions. Financial analysts are actively looking to utilise this data appropriately, yet some challenges can affect their ability to use and manage the information collected promptly.

While an organisation may employ the right people, have the correct structures and goals in place to deliver data-driven strategies, they may fail to utilise the full value available within this data. Studies emphasise this further, suggesting that BI tools are well-resourced, but are not being used to their full potential. Findings suggest organisations could be potentially wasting their investments, with a large proportion of data analysts believing they have clear methods of improving company profits but lack the time to implement more strategic plans.

One of the biggest challenges on the productivity of an analyst is the time allocated to accessing disparate data sets. It may be a relatively easy process, it is known to take a large proportion of an analyst’s time. Other reports have suggested that a large proportion of analysts have found that the data sources they require to do their jobs effectively are often inaccessible, broken or only available on an intermittent basis.

Inaccurate data sources can lead to delays and bigger impacts on the business. Delays in the process can result in analysts using reports and making decisions based on dated information. In the current climate, a time of unpredictability and constant change, it isn’t efficient and possibly risky to be using dated financial data as the tool for making accurate plans.

Determining data from multiple sources

Data analysts use several data sources which can be challenging, particularly when a business is continuously updating their operational and management focus. Schema updates are often implemented by analysts which do improve reporting accuracy and decision making, but these changes result in further time and often delays the entire process.

To enable finance times to spend more time on analysis and less time on data preparation, businesses should re-evaluate their data systems. By applying automation to selective processes, businesses can eliminate a large number of the barriers data analysts experience today. The information can then be replicated, transformed and embedded into one data set and used to make data-focused decisions.

The surge of BI tools in business is positive, but the disjointed approach towards data analytics makes it difficult for financial teams to get the most out of the BI tools available to them. Studies clearly show that the technology and professionals using the tools are not being utilised to the full effect. Instead of looking to increase the number of data analysts in your team, a business should focus on how it can combine multiple sources of data and look to improve data access and management processes. Once access to real-time data is made simple, data analysts can extract all of the data value from BI tools to ensure their business remains competitive and customer experience stays high.

The global leader in AI solutions for finance AppZen is launching Mastermind Analytics. The new service is an innovative analytics AI service that identifies spend risks and generates a set of recommended measures. The platform provides finance teams with the necessary information regarding trends and a clear assessment of success or areas that require additional work.

AppZen has experienced a surge in demand as CFOs manage a number of digitally-focused transformation plans with their finance teams. The AI service provided by AppZen will streamline processes for finance professionals and at the same time, reduce the costs for businesses looking to enhance their automation tools. 

Mastermind Analytics provides finance teams with relevant insights into investment, risk and overall performance, allowing employees to really focus on the most important elements. The new service reduces time-consuming processes and offers metrics on how to enhance particular processes and overall efficiency. The Mastermind Analytics features include:

-Complete visibility of behaviour trends

-AI-focused insights, combined with accurate spend data information based on reliable documents

– A complete view of a company’s finance

-Multiple charts including details on cross-system spend, risk and overall performance

-Benchmarking analysis against other competitors

-Dashboard service options for specific cases, metrics and other teams

Anant Kale, the CEO and founder of AppZen announced the new service and is excited about the analytics potential available within the AI software of products.  Kales explains that finance teams have access to information that was previously unattainable, enabling clarity into activities that could determine and prevent potential challenges for a business.

Mastermind Analytics involves minimal integration or configuration and allows for simple changes to an existing dashboard. Users can develop their own individual dashboards and charts with little or no IT support.

AppZen designed Mastermind Analytics in order to provide finance professionals with the confidence in how their performance compares to other businesses by displaying what needs to be their focus, and what really needs improving. The insights, charts and other metrics provide a clear and simplistic representation for finance professionals.

Artificial Intelligence and Machine Learning are continuously transforming businesses and influencing traditional processes in the finance industry. AI technology already supports many daily activities, and quite often does this without us knowing.

A recent study by Gartner suggests that 40% of major businesses plan to implement AI solutions in 2020 and over half intend to double their existing services during this year. Admittedly these forecasts were made before the pandemic, but analysts believe the rise of AI will inevitably continue.

In certain industries, AI and ML offer a wider range of opportunities. One particular sector this relates to is in finance, where new technologies are already having an impact and altering the traditional shape of the financial industry. Some businesses are taking full advantage of AI solutions in the most effective manner. This enables businesses to utilise the potential of new technologies and improve their processes.

Risk Management

AI plays a critical part in risk management and this is particularly important for the finance industry. For certain cases of risk, algorithms can be implemented to measure case history and determine particular problems. This involves using ML to generate certain trends and identify potential risks.

The use of ML in risk management means a significant amount of data can be processed more quickly. For example, structured and unstructured data can be managed via cognitive computing. Processes like this would translate into many hours for a team to work on.

Fraud Prevention

With a rise in digital customer transactions in the last few years, providing an effective fraud detection model has become an important part of protecting sensitive information. AI can be implemented to enhance existing rule-based models and support human analysts, providing more efficient, accurate and cost-effective results.

Personalised Banking Service

In the banking industry, smart chat features supported by AI can provide intelligent solutions for users and reduce the overall workload for associated companies. Voice-activated virtual assistants are continuing to grow in popularity. These services are capable of checking balances, account activity and scheduling payments.

Many banks now have applications that provide personalised financial support and help in meeting financial goals. These AI-driven systems can monitor income, expenses, spending behaviours and provide financial support. Many banking applications can also provide reminders to pay bills, transactions and offer a more interactive and convenient service.

Quantitative Trading

Quantitative trading or data-driven investment has been expanding within the global stock markets in recent years. Investment companies rely on data to generate accurate predictions and determine future patterns in the market.

AI enables the added advantage of measuring trends from previous data and making predictions on whether they are likely to happen again in the future. When there are particular disruptions in the data, AI can examine the data in more detail and understand certain factors that may have influenced this change and be more prepared for the future.

Credit Decisions

In many industries, AI is effective in enhancing the decision-making process. In terms of credit, AI offers accurate information on potential borrowers, presenting key details quickly and at a lower cost. AI credit scoring is more detailed and can identify applicants who are more likely to default and others that may not have a suitable credit history. AI models also lack the human element which means they are unbiased and not influenced by human decisions.

Systems that are driven by AI can be implemented quickly and are likely to become more efficient and reliable. The services are emerging more within the finance industry and are being actively integrated into more businesses operating within the finance industry. AI holds a lot of potential for the finance market but it is up to each business to implement the right technology and make the smartest decisions with the right data.

Analysts are instructing investors to focus more attention on Anaplan as a growing business within the digital transformation marketplace. In the last few days, Anaplan reported significant growth based on a surge of demand for its cloud-based business planning software. Businesses both big and small recognise the necessity to implement digital technology and the need to enhance their workflows to remain competitive in the new cloud industry era.

Anaplan provides a variety of software tools to enable managers to plan where resources should be allocated and all of this can be done in real-time. Managers are equipped with a deeper level of information fuelled by innovative machine learning and analytical sources. The finished product is an insightful pool of information that allows for more efficient business decisions.

Smart and effective planning is more important than ever. Implementing a cloud-focused, connected planning approach to business is a smart move and businesses of all sizes are moving to Anaplan as their preferred supplier. 

A core part of the expansion at Anaplan is a system called Hyperblock which is a unique database that allows users from across the network to make regular updates, enabling a more accurate and efficient modelling process. Since 2017, annual subscription sales have expanded by 57%, with the number of customers with $250,000 of annual billings increasing 181 in 2018, to 353 in 2020. Total annual billings increased up to $417 million in 2020 by up to 44%.

The surge in results, according to Calderoni, the CEO of Anaplan is reflective of where the enterprise industry is moving. Digital transformation is rapidly accelerating and Anaplan is continuing to find new customers and enhancing its sales prospects with its existing clients.

Starting this month, Anaplan will be hosting a virtual connected planning Xperience conference. The objective of the 7-day confab is to deliver a range of new predictive analytical tools based on artificial intelligence. Combined with Hyperblock, these services will enable Anaplan to continue transitioning from simple business planning software to true business modelling. Industry analysts believe it is a major move by Anaplan. The tools will make Anaplan software essential for businesses looking to stay on top of market trends, make key decisions quicker and reduce business risks. 

Shares with Anaplan increased to $62 last week and the overall stock is up 17% in 2020. 

A number of leading corporates including Amazon, Allianz, Microsoft and S&P Global are heading a new project to combine AI, open-source analytics and open data to enhance the management of climate risks and take advantage of the new opportunities within the net-zero transition.

Managed by the non-profit organisation the Linux Foundation, the LF Climate Finance Foundation (LFCF) intends to enable investors, banks, insurers, businesses and NGOs to utilise AI-driven data, allowing businesses to manage the financial implications related with climate change.

The LFCF intends to create an open-source climate data platform referred to as the OS Climate, incorporating a number of physical and economic risk scenarios and a series of financial and economic models.

With a greater level of data, the goal is for members to work together towards delivering predictive analytical tools and investment products that are capable of managing climate-related risk and finance climate solutions across multiple areas. The Linux Foundation highlights that there is a clear urgency from a number of pensions funds, banks and governments to enable public access to corporate climate and sustainability data needed to support targets laid out in the Paris Agreement, as well as more sophisticated insights to enable better financial decisions.

The planning team of the newly formed foundation includes a number of representatives from green organisations such as WWF and Ceres, who are all expected to be involved in selecting climate-related businesses, infrastructure and capital projects.

Jim Zemlin, the executive director of the Linux Foundation explains that the costs and challenges associated with the analytics for climate-focused investment plans require well-managed teams and resource sharing among many areas. The LF Climate Finance Foundation will allow shared development from a number of leading financial organisations, academia, governments and NGOs.

The LFCF hopes that investors and banks alike will be able to utilise the platform to assess portfolios and separate investments and support the identification of climate risks. Governments could also use the system to measure and identify resilient infrastructure investments and create appropriate policies.

Jennifer Yokoyama, the chief IP counsel at Microsoft explains that in order to address climate issues in an effective manner requires people and businesses to have suitable access to data and a clear understanding of the impacts of their actions. Presenting and sharing relevant sustainability data via the LF Climate Finance Foundation will enhance financial modelling and the overall knowledge of climate change impact.

In a move to expand its services further, the British software provider Aveva Group has confirmed the purchase of SoftBank supported business OSIsoft for a total value of $5 billion.

Industrial businesses have started integrating additional software into their manufacturing to reduce costs and improve their overall supply chains, benefiting businesses like Aveva.

Aveva has stated that it plans a rights issue valued at approximately $3.5 billion to support the complete purchase of OSIsoft. OSIsoft makes software that captures data from ships, chemical boilers, power plants and other sites in industries within oil and gas, mining, paper and water.

According to Craig Hayman, the CEO of Aveva, the acquisition of OSIsoft will accelerate the Enlarged Groups position in the digitisation of the industrial world, which is being strengthened by a rising need for sustainability, cloud, data visualisation and artificial intelligence.

The last significant deal made by Aveva was with Schneider Electric over three years ago when they secured a 60% stake in the business. The British based business stated it would fund the deal using a mix of rights issues, cash on balance sheets and new debt. OSIsoft’s PI system which collects, assesses and shares data from a range of sources, will form a separate business unit of the extended company. 

Patrick Kennedy, the CEO and founder of OSIsoft will continue to remain involved in the business after the takeover through being appointed as the new chairman.