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.

The demand for data-driven technology and innovation in the financial services market is surging, as data continues to take a more important role in the operations and management side of a business. From enhancing processes and retaining customers to improving security, data-driven technology has evolved into an essential technological solution for several challenges facing the finance industry. With proven success, more finance-focused businesses have been moving towards the data industry and implementing new data-focused services wherever possible.

Financial services businesses are keen to utilise the opportunities data can offer. The main challenge that needs to be addressed, however, is that the capabilities of data depend on the quality and availability of information.

Managing data as the first step

Data management is a critical step in leading to later stages of data innovation. Financial services businesses will tend to invest in several data-driven solutions to improve compliance, risk and security. To effectively deliver and utilise the information, businesses need to ensure their data is current, accurate and reliable.

One good example of this relates to tooling systems, such as reporting or another data-focused process. These processes need customer data, but often the existing flow is not complete or possibly not entirely accurate. Quite often, as a temporary measure businesses will have their team manually update and correct any data anomalies. This can become challenging when the information from new services do not provide the anticipated results. 

Small steps can make the difference

Fortunately for financial services businesses to rectify and ensure data management is correct does not necessarily mean high budgets. Beginning with goal definition has been a proven method on improving data governance and ensuring a business can leverage data management capabilities further down the line.

Delivering value can only happen when businesses can generate meaningful insights and high quality, accurate data. To achieve this, an organisation should begin by defining their data requirements by determining specific goals. A maturity assessment enables quick bottom-up analysis of the main challenges a business faces. The relevant data management and governance services can then be created and utilised, to structurally improve the data problems a business may be facing.

How to approach data management and governance

Industry analysts recommend that financial services organisations tend to implement several actions to ensure their data-driven technology begins correctly. This includes:

Defining clear goals – define the data challenges that you intend to solve and assess any inconsistencies between finance and risk reporting

Measure the impact of data management – identify data flows from source to report to measure specific areas of data problems. Data flow analysis will reveal details to determine what data gathered from finance and risk is inconsistent.

Create a custom data governance solution – Create data management solutions that fit your business, such as data quality assessment and selected mitigation measures.

Measuring and assessing data solutions – Measurement is vital to determine success. Measuring the improvements after implementing new technology services is critical. A business should maintain consistency between finance and risk data, enabling accurate risk and finance reporting.

In today’s market, the volume and importance of data continue to increase, meaning any data-focused asset requires management and protection. Now is the time for financial services businesses to ensure data management is a top priority in their business plans.

Oracle has stated that it is the first public cloud provider to launch a security policy enforcement plan incorporating best practices, with Oracle Maximum Security Zones.

Oracle Cloud Guard assesses configurations and general activities to detect potential threats and automatically responds and repairs any faults across all Oracle Cloud global regions.

The rise and transition towards cloud technology have created a number of new security ‘blind spots’. As a result, there has been in excess of 200 security breaches in just the last two years. Recent studies from Gartner have forecasted that by 2025, nearly 100% of cloud security failures will be the fault of the customer. Clay Magouyrk, executive vice president of Oracle Cloud Infrastructure explains that security has a core part of the design plan in Oracle Cloud for many years. 

Magouyrk believes that security should be a necessity that is integrated, instead of making customers decide between security or a lower-cost service. Magouyrk explains that Oracle Cloud Guard and Oracle Maximum Security Zones security automation and embedded knowledge means customers can be assured when operating their business workloads on Oracle Cloud.

Oracle Cloud Guard operates as a log and events system that automatically integrates with all major Oracle Cloud Infrastructure services i.e. Compute, Networking and Storage and adds specific, unique elements referred to as targets, detectors and responders.

Oracle Maximum Security Zones goes beyond IaaS access management to block insecure actions or processes by using a new policy tool that is applicable to certain selected cloud tools. Oracle believes the new service will ensure all resources are secure from potential threats by enabling the best available security practices.

Jay Bretzmann of IDC cybersecurity research explains that as workloads move to the cloud, businesses are actively looking for a supplier where security technology is created throughout the entire hardware and software chain. Bretzmann believes the new cloud security services will support the automation and enhance the management of vital applications with strict security and compliance standards, that many believed would not be available off-premise.

In the finance world, legacy systems have represented the foundation that has maintained a business in its traditional activities. Making the decision to move away from legacy systems can be complicated, from a technical and business culture perspective. Many analysts believe that while it may be a challenging move, it is something that must be faced, particularly at a time when organisations are accelerating their business transformation plans.

There is a range of complexities that need to be considered with this move away from legacy systems. From a technical point of view, it can be difficult to extract data from traditional systems and the associated applications that are operated on these facilities. CIOs emphasise the complicated dependencies that have grown over the years as a consequence of implementing a number of various databases and other systems. Nevertheless, ignoring the problem will result in gathering additional IT technology issues for the future, especially as businesses actively seek to find talent to manage the older systems.

Agile and flexible platforms, equipped with data-focused business models and lower overheads are positioning themselves to challenge the traditional corporate models. The financial industry is highly competitive and businesses need to utilise every possible part of agility and information they have to compete with other names, as well as newcomers to the marketplace. There are a number of new organisations capable of disrupting the financial industry. For those companies that contain high volumes of data and lack the modernised approach, there is a likelihood that they lack the ability to assess all their data and respond effectively to new demands and changes in the market.

Innovative business leaders in the finance world understand this challenge but may be struggling to implement the required changes. As a consequence, a business experience considerable fragmentation of their data across various platforms. Many CIOs are making the move to the cloud to attempt to improve the situation, but issues of data fragmentation can continue. While there are multiple benefits in moving to the cloud, many businesses experienced data fragmentation across clouds, making the overall situation actually worse. Fragmentation results in less secure data and the potential for infrastructure costs to spiral.

Taking a more modern approach to data management

The solution to all of this lies in taking a more modern approach to data management. Financial industry businesses to utilise a more software-focused platform approach to managing their data. This will focus on consolidating and managing workloads and data through one singular system. 

FInancial businesses should explore other opportunities to enhance compliance through utilising applications that are located on the platform. These applications measure personal data and play an equally important role in data governance. Businesses can also introduce other applications to improve security or use analytical systems to enhance the overall customer experience. Applying these techniques can help businesses stand from newcomers to the market that is not hindered by legacy systems but lack the volume of data and customer bases.

The financial market is relatively unique in that it still consists of businesses that have existed for many years. However, in order for these traditional companies to survive, they will have to adapt and modernise. The first place to focus on, and probably the most valuable resource they contain is their data.

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.