CFOs focus technology investment plans on attracting new AI/ML talent
January 26, 2022
Recent News & Insights
How big data is transforming the fintech industry
January 21, 2022
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.
Recent News & Insights
How CFOs enhance performance with data and automation
January 14, 2022
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.
Recent News & Insights
The new era of AI is focused on delivering better decisions
January 7, 2022
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.
Recent News & Insights
Focus areas for data and analytics in 2022
January 4, 2022
Like all technology, big data is progressing, and as we start the new year, it is an ideal time to explore what opportunities exist and what areas need improving. This year represents a critical time for big data, AI and analytics, with more businesses anticipating progress and better results for their organisations. Here are some of the areas to consider in big data this year:
Creating a data retention policy
Many businesses have overlooked discussions concerning big data retention or failed to make time to tackle this area. With global data expected to increase considerably in the next few years, and big data making up the bulk of that information, 2022 is the time to create big data retention policies and disregard the data not needed.
Defining the role of big data in the wider data landscape
To establish information across an organisation and ensure data is available for everyone for analytics and decision making, IT teams should ensure big data and other structured data in a business connects and links to all areas.
Utilising additional no-code analytical applications
Using no-code reporting tools for analytics and creating additional reports quicker for end-users and reducing work pressure on IT teams.
Reassess the true value of current applications
While it’s a positive step to launch analytical tools, businesses must ensure it works as well for the organisation as it did a few years ago when it was first introduced. Businesses are evolving, and it’s likely requirements will change in terms of what analytical solutions a business needs now compared to a few years ago. This year it would be beneficial to review the effectiveness of existing analytical tools, measure their performance and assess whether they are meeting the needs of the business.
Create an application and data maintenance strategy
As with structured data and other systems, those utilising big data and analytics require consistent maintenance. Yet many businesses implementing analytics and big data lack any structured processes for maintenance. Big data and analytics have reached a level where maintenance processes are needed.
Upskill and Training for IT
To support big data and analytical operations, new IT skills are necessary for IT professionals. Training may include further development on data science, analysis, big data storage and focusing on skills with new tools, such as no-code analytics.
Assess privacy, security and trusted sources
Big data can be acquired from several third-party sources. These require constant reviewing to ensure they meet corporate security and privacy guidelines. This review should also apply to internal data within a business.
Measure vendor support in big data and analytics
Many vendors provide big data and analytical tools but do not offer the support required for a business. It’s vital to work with vendors that generate sufficient support for your team and additional guidance for important projects.
Ensure your big data and analytics supports the overall customer experience
Nearly every business is committed to improving the customer experience. A core part of this process is establishing customer-focused automation and assisting customers in getting requests, questions and answers. Automating customer-focused systems that use NLP and AI to understand customer behaviours and engage in conversations are still in a stage of development. Businesses that focus on enhancing NLP and AI performance within these areas will undoubtedly see the benefits in the future.
Leaders must review big data and analytics plans
As these technologies have matured, it is now time for senior leaders and other stakeholders to reassess the progress of AI and analytics and ensure a business has to secure support from the top.