AI can leverage a bank’s biggest asset: its data. This can provide traditional finance businesses with a new source of potential income.

It’s clear today and technology matters in the finance industry. The new emerging fintech displays the power of integrating technology with finance. It’s understood that some of the leading businesses such as Monzo and Revolut have succeeded in securing large numbers of customers predominantly because they were one of the first to automate the process of creating a bank account, replacing the traditional time-consuming way of setting up an account.

An automated process like this involves managing data, and as this becomes more advanced, it is often referred to as artificial intelligence (AI). Chatbots represent one of the most common forms of visible AI in finance. WeBank of China reports that nearly 98% of all customer enquiries can be managed via chatbots. Aside from the overall customer experience, AI can enhance finance systems, reduce costs and improve overall margins.

Data represents the biggest factor for conventional businesses to com[pete against fintech. Incumbents are gradually transforming in terms of data and digital technology. Their size and availability of resources provide traditional finance with a significant advantage over fintech and can allow them to catch up relatively fast.

Traditional finance businesses are investing rapidly in AI solutions, with banking scoring the highest of any industry for adopting AI, based on a recent study by GlobalData. The data incumbent finance businesses have gathered through their long years of building a customer base enables a relatively quick closing of the gap if applied with an AI strategy. Once this gap with fintech is closed, the new businesses may not have as clear a competitive edge as before. The Financial Times recently stated that the current performance of fintech banks during the pandemic suggests the concept that leading fintech companies can do anything conventional businesses can do better is diminishing. While fintech has had the initial advantage in terms of technology, it will need to continue innovating and enhance its product offering beyond its existing basic features.

Industry experts believe there is better technology available than apps. The digital-only platform, MyBank provides an example of how AI can generate new options for finance. By 2019 MyBank had launched the 3-1-0 model, a business loan that takes under three minutes to apply and less than a second to approve, with no human intervention required. When used in the right way, AI can reduce the time taken to make a loan approval and at the same time, ensure loans are more effective by lowering the non-performing loan ratios. Other businesses have applied their historical data from existing customers to develop a predictive model and determine the key variables that account for certain factors like missed repayments. Implementing this kind of process is not possible for new banks that lack past information.

Protecting finance data with AI

The more data acquired, the more responsibility you have. Finance data consists of some of the most private and sensitive information. It is therefore critical finance controls this data and AI delivers another layer of protection against potential cyber-attacks.

Several finance services businesses have incorporated machine learning into their security systems. Some have struggled to combat advanced cyber attacks with groups with access to their ML technology and managing their fraud detection rates, with high levels of false-positive alerts daily. Controlling false positives in financial security is a significant issue. Monzo, for example, has come under scrutiny for blocking customer accounts for extended periods because automated software has detected signs of potential criminal activity, and they lack the human staff to manage the backlog.

AI and deep learning systems have reduced this level of false positives and the overall level of fraud detection. These improvements have enabled the finance industry to focus more time on potential fraud, improving its security and enhancing the overall customer experience.

While there may be challenges and concerns with automation, the positives of giving more time to employees due to AI is valuable. In the scenario mentioned, fewer employees focusing on false positives means more satisfied customers and additional staff managing actual cases of fraud.

Whether referred to as fintech or banking, the case of managing money focuses on people and data. If data is handled effectively, people can create accounts, deposit and spend their money easily. When people apply for a loan, the process will determine that the right people are approved, and others declined, and there is transparency for both sides to understand their results.

The most effective data processes available today predominantly include AI technology, and this is the case for the finance industry.

As finance businesses continue to evolve their services and offerings, a new approach towards data and analytics is needed to meet rising demands.

Big data and analytics are important elements of today’s finance industry and go in collaboration with the challenge in improving the capabilities of data management for finance businesses. The key question is how do data analytical tools and technologies add further value to the financial services industry?

New data-focused services can improve revenues and overall costs reduced, improving competitiveness. Security can provide customers with enhanced and safer services. Successful AI-focused businesses target their service to the new era of tech-savvy millennials. 

Emerging fintech is unravelling the power of big data to determine customer behaviour and create structured risk assessments, which can set them apart from other established financial institutions. The speed of real-time data enables disruptive fintech and challenger banks the ability to adapt to a rapidly changing industry, along with a more comprehensive understanding of customer relationships.

Customer-focused analytics has become a key priority. This is a significant transformation from the past when the financial industry was mainly product-focused. Data insights, systems and operations are focused on the customer. As a result, it’s critical to understand how to determine changing markets and customer requirements.

Risk management has improved and digitisation has established the way for automation by increasing agility and innovation, as well as generating revenue for data. Data analytics has also made regulatory compliance simpler by establishing a platform for a business and enabling real-time frameworks with regulators. Realising the value of big data requires an analytical perspective as this support transforming data into valuable insights.

This is where a data analyst comes into play. Big data analysts recognise this process and what information to look for that will translate into value and enhanced customer satisfaction.

Rates of adoption for business intelligence platforms are rising as more businesses integrate with big data, seeking to enhance findings from large data sets. Digitisation in finance has allowed disruptive technologies such as advanced data analytics, AI, machine learning, big data to transform how fintech can compete in the market. Data analytical tools have become more sophisticated and so are more necessary to businesses today.

As the finance industry works towards a data-driven priority, businesses need to react to these changes in a structured manner. Those looking to remain competitive must be willing to adapt and understand how to use data skills on the job.

The finance industry is actively looking to harness the insights from several data sources but existing limitations within their IT infrastructure. A new industry standard intends to make it easier for financial services businesses to manage data in the cloud, opening the potential to a surge of new service and product development.

Cloud services facilitate the technical efforts of sharing data by moving the responsibility for developing, securing and maintaining information to cloud-managed service providers. Up until now, the finance industry has had very few ways to verify that cloud providers can meet their strict requirements in terms of governance and security. The new CDMC standard, announced by the EDM Council, focuses specifically on these concerns.

The CDMC delivers a set of measures to ensure cloud environments meet the security and governance required for regulated industries like finance. The standard was developed by a working group in collaboration with Morgan Stanley, Refinitiv and over 20 financial institution leaders and cloud providers. The standard goes beyond data sharing and explores the needs of financial services businesses as they shift their operations into the cloud. Data remains an important area where finance businesses can improve their response to customer requirements. A standard that supports their transition to the cloud will only enhance their capacity to digest and share data with a wide range of customers.
Traditionally, data feeds going in and out have typically been custom created and managed individually. Finance businesses are under growing pressure to innovate and deliver enhanced services due to the growing number of new fintech companies, but they are attempting this with certain limitations.

Cloud data platforms handle this challenge by managing the technical complications of sharing and securing data within a business. Cloud providers must display their following to the new standard via an independent group.

CDMC presents several opportunities by accelerating the rate finance businesses can transfer their operations to the cloud. A vital result of this is the potential to apply financial data from various silos, enabling companies to create new revenue streams through innovative products and work together across the new data economy. Multiple data sources translate into easier access and sharing of data between teams and the wider business. Aside from that, it enables the entire industry to be more connected and share data in innovative ways with customers and their partners.

Tackling fraud is a particular challenge, especially as every financially related business is trying to combat this problem by using their data. With the potential to utilise data from multiple sources and the ability to explore activity in banks and payment processors, there’s a lot more potential to identify and tackle fraud.

This model has worked for the security industry, where trusted providers explore activity across participating businesses to detect threats. With a structured system enabling financial data to share in the cloud, a new wave of service providers can handle the pressure of detecting fraud for financial businesses and likely do it more effectively than before. This system requires considerable collaboration, but the CDMC has created the foundations of trust for these services to be delivered.

Another area where data sharing in the cloud can play a significant role is in ESG. Businesses that manage pensions and sovereign wealth funds are under growing pressure to ensure investments meet ethical and environmental criteria that are constantly requested by investors and regulators.

Defining whether a fund has a positive environmental score or performs business ethically can be challenging to determine. Third-party providers are capable of consolidating this information and effectively creating an authoritative voice for the industry. The CDMC standard applies a similar baseline of trust to ensure shared data services are available.

These are examples of how data sharing in the cloud can enable finance businesses to transfer their focus from developing varied IT services towards an innovative system that focuses on progress and remaining competitive. There is a range of third party data providers available in the cloud that can improve predictive analytics and generate a more personalised customer service.

Combining this data can enable finance businesses to improve their predictive analytics and deliver a more bespoke service to their customers, but this requires a relatively seamless data process. Managed cloud services simplify this process, and the CDMC represents a vital step in delivering a more efficient cloud migration system for the finance industry.

Customer-focused and inclusivity remain challenges for the finance industry. AI-powered analytics can support businesses in differentiating themselves and enable them to create a competitive edge through delivering personalised experiences for their customers.
Such technologies like AI enable businesses to create a strategic focus and implement analytics as a valued business measure. The analytics and business intelligence software industry has grown significantly in the last few years. Finance has been an early adoption of BI and analytics to drive further growth, reduce risk and improve costs.

With the rapid rise of digital banking, the speed of transaction data has increased significantly. This data has revealed opportunities for businesses to gain insights into customer behaviour and create custom offerings that leverage analytics as a vital service. These new developments include determining key variables such as the high-value customers, who have the highest potential to create revenue growth? Can a business develop an early warning system to detect fraud? Data analytics is continuing to provide the answers that financial leaders need to navigate this challenging environment.

According to a Deloitte survey, the early adopters of this technology benefited from the quick recognition of how analytics can influence the success of a business. This adoption has supported companies with delivering a carefully constructed analytics plan that incorporates AI within an organisation. The survey suggests that these businesses focused on strategies for AI adoption for the teams, clearly recognising the strategic influence of AI in their business.

According to a 2020 McKinsey study, AI technologies can drive revenues by improving the personalisation of services to customers, reduce costs by applying further automation, reduce rates of errors and enhance resource allocation. AI can generate new opportunities based on the ability to create insights from large data sets. The possible value for banks is one of the biggest across industries, as AI has the potential to unlock nearly $1 trillion of value for them every year.

Appreciating the need to go mainstream with AI, international finance businesses are beginning to harness the power of data to generate vital insights. AI and analytics will gradually become an element of every major initiative. It will become a regular part in areas from customers and risk to workforce and supply chain.

Personalised experiences and products created by advanced analytics and ML will be essential in attracting customers in such as competitive landscape. Financial businesses have the opportunity to reduce the barriers and focus on the analytics journey before the early adopters extend the gap even further.

Recent research by Deloitte suggests that CFOs should be taking the lead in leveraging corporate data not only for their own decision-making but also to strengthen their impact beyond their core finance functions. This means CFOs can apply big data not only for financial plans such as budgeting and forecasting but also to increase their reach into other areas such as supply chain management and even customer interactions.

The Deloitte survey highlight who tends to be in charge of data analytics across various businesses. Over 20%, the business or divisional head was the leader of analytics and typically had control of the budgets assigned. CFO’s came a close second, at around 18% of businesses surveyed.  Finance was considered as the most likely area to invest in analytics, and this was the case for nearly 80% of businesses involved in the study. 

Keith Taylor, the CFO of digital infrastructure company Equinix explains that the pandemic has resulted in financial directors having to step up and play a wider role in shifting organisations from traditional working models to ones that provide access to real-time insights and allow for better decision-making.

During the pandemic, CFOs were forced to swift and decisive action to protect their business. They must now focus on the future and explore the most effective ways of delivering an environment that supports a quick recovery. A vital part of this recovery will be good access to real-time information regarding key performance indicators capable of supporting necessary decisions. Taylor highlights that while data generates insights, it also creates unnecessary details and so businesses must ensure their insights remain targeted and avoid any misinformation. CFOs will have to invest in data strategies to ensure they can manage and validate information to the highest standard.

Big data has become a vital part of the finance function, with senior members such as the CTO actively involved in this area. Global businesses today are multi-locational and work on multiple business units, and so it’s important to have a singular version of the truth, rather than managing data on a local or business level.

A CFO needs to be capable of understanding data on a global scale to ensure they can challenge all business areas. Centralising data into one area enables users to access the information via one common set of dashboards. This centralised approach means data can be explored at a global level by the CFO to understand each country’s performance. Team leaders and consultants can then drill into the same single data platform to access the most relevant data.

Analysts believe that businesses need to introduce more analytical processes and use raw data to generate business-focused information. Big data will only become more important as we progress forward. It’s important to actively explore all of the opportunities available by leveraging intelligence from data analytics.

Data science measures can determine important events in advance in order to allow preemptive action to create that all-important competitive edge. However, implementing the tools and technology capable of exploring this data effectively is important. Some progress has been made in this area but many businesses need to continue investing more in data science measures. For example, innovative analytical tools can predict customer churn within an organisation and how this may impact the business in supporting investment and planning decisions.

These tools would also allow CFOs to invest in the most suitable infrastructure, in the right areas of the business, to generate the best return on investment.

The launch of the Global Open Finance Challenge has caught the attention of the fintech market. In a recent conversation with Finextra, NatWest Group highlighted the reasons behind introducing the initiative and the inclusive incentives being used to attract the best teams.

CIBC, Itaú Unibanco, National Australia Bank and NatWest Group have partnered to host a virtual event due to take place in October 2021 and is focused on delivering a stronger, more advance banking and finance industry. With support from Amazon Web Services (AWS), four major banks are encouraging the best innovators to launch large-scale new customers solutions. Each bank provides shared APIs, with a combination of open banking, open financing and experimental services. Teams will then have the chance to test, build and validate their solutions.

Paul Thwaite, the CEO of commercial banking at Natwest Group, explains that API’s have already proven to be vital for business and corporate customers, enabling open banking payments and reducing cases of fraud.

Daniel Globerson, the head of the bank of APIs at NatWest Group, explains that his position alone highlights that open banking is a significant area of opportunity. While many institutions viewed open banking as something to comply with, NatWest Group regarded it as an opportunity to create products and services in new and innovative ways. It represented a chance to collaborate with new and emerging fintech, to strengthen customer relationships and improve the overall user experience.

Globerson believes that open finance is just another step on our journey towards smart data. Worldwide, we are experiencing a growth of open banking, customer data rights and other regulations to support innovation, competition and ensure the customer remains in control of their data.
Open finance can offer businesses more creativity in meeting customer needs by leveraging new data sources and creating a more comprehensive picture of customer financers. Having a complete picture of financial health is a vital step in really understanding what customers need. Banks are more than just accounts and payments, but also institutions of trust. There is an opportunity today for financial institutions to build trust and improve the overall customer experience.

While opportunities within banking and open finance exist, Globerson refers to the position financial institutions are in and are trying to hold in an environment that is more open now to innovation and agile businesses. Globerson believes financial institutions are in a unique position where they have built trust from customers with data and privacy and a safe store of funds.

While many institutions have been operating for many years, the fact that groups like NatWest have not focused on monetising data in return for services provides a real competitive advantage. It’s critical that banks are trusted with data and privacy and that financial success doesn’t focus on monetising the data of individuals in return for offering the services. By leveraging this level of trust and longevity, combined with innovation and new partnerships, financial institutions are very capable of competing and progressing in this new finance world.

The opportunities presented through data analytics have extended the reach of financial reports and analytics, but is this enough for CFO’s today? Financial analysts and CFOs are notoriously focused on data for reporting, risk assessments and for determining possible scenarios.

One of the primary issues that CFOs now isn’t the lack of access to reports for financial decisions but the challenging process in generating these reports. This laborious manual activity generally involves a team of financial analysts aggregating and incorporating information into spreadsheets to apply to particular questions and scenarios.

When applying this approach, CFOs lack complete access to consolidated reporting and the availability of all insights from the data in front of them. By taking this approach, analytical tools can come into their own and make data easier to navigate and appreciate promptly.

To reach this point, data from multiple areas must be consolidated into a singular database via an automated system. This approach will save financial analysts considerable time and eliminate the potential human errors associated with this process. The final result is a dashboard containing a data summary and the capability to look into each data set in more detail. This approach allows finance teams to generate multiple reports and scenarios based on this accurate and organised data.

There are cases where finance teams have spent considerable time consolidating financial information from various data sets manually. After exporting and reconciling data, finance teams would spend further time ensuring the information was accurate and well prepared for the company to use. Transferring to automated data consolidation enables teams to make instant comparisons and create reports through one platform.

Switching to automation may not be the solution for all of the reporting requirements in finance but data consolidation combined with automation can generate more data from multiple sources quicker and save employees considerable time. The biggest challenge with applying this process is the caution of many businesses to place their trust in automated data consolidation. Financial executives have been reliant on generating reports manually via spreadsheets for years and are hesitant to transform the entire system in such a short period.

This cautious approach is a big reason why IT and other technology leaders must know about business changes when planning to implement automation for analytics. As with many analytics and automation plans, finance must be a core element in the project and be a priority in how businesses process change to take full advantage of automation.

With the support of automation, a data consolidation is a vital tool in transforming how finance does business, but implementing it is critical to a successful transition. IT and Finance are vital in launching a new process and ensuring it receives full support across the business, starting with the CFO.

The influence of regtech will continue to grow as businesses in the finance industry manage the increasing challenges and pressures of remaining compliant. The regulatory environment is growing and becoming more complicated. To remain compliant with continuing evolving laws and the rise of big data RegTech is becoming a focus area. What do this offer to the finance industry and its future?

The finance industry is under pressure to maintain data safety, privacy and to continuously monitor cases of fraud or illegal activity. Regtech incorporates technologically advanced solutions, applying software and data to ensure a company’s journey towards compliance is more effective. According to Deloitte, true regtech includes the following features that make it different from other compliance solutions:

• Agility: Datasets can be arranged and organised via ETL methods
• Analytics: Innovative ways of exploring big data providing detailed analysis for potential risks.
• Integration: The solution enables instant operation
• Speed: Real-time reports and customisation allow effective feedback and the ability to adapt when required.

The Regtech cloud-based solution provides a flexible deployment that can adapt to the needs of a business. The main purpose is to provide a service that eliminates the effort and risk businesses face in remaining compliant.

The finance sector is heavily regulated, requiring compliance with various organisations and even governments. Regtech includes several benefits enabling businesses the flexibility to focus on enhancing customer experiences, creating new services and solutions and implementing long-term planning options.

The long term goal of regtech is to create a transparent financial system capable of adapting to possible disruptions. Some examples of this system in action are within the information-based compliance area, including identity verification and regulatory reporting.
With further support through government funding, private investment and additional research, regtech can have significant impacts. Data continues to grow, and the demands in the finance industry to manage this increased volume of data is becoming more challenging.
The influence of regtech will continue to grow as businesses in the finance industry tackle the growing challenges and pressures of staying compliant. The startup industry is open to new solutions to these regulatory hurdles, and the results could create transparency and trust with the customers.

The Pareto Principle refers to the concept that 80% of consequences are generated from 20% of causes, meaning the remainder is less impactful. Those working in the data industry may have a different version of the 80-20 principle. A data scientist generally invests approximately 80% of their time cleaning up data instead of working on actual analysis and delivering key insights. While many data scientists spend over 20% of their time working on data analysis, they will inevitably spend numerous hours organising information. This process can include removing duplicate data and ensuring all information is formatted appropriately. 

Studies suggest that an average of 45% of the total time spent is on this workflow. Another report by CrowdFlower puts the estimate even higher. Preparing data is vital, but inappropriate information will generate inaccurate data if not handled correctly. The main question asked is ensuring a data scientist’s time is allocated to necessary tasks rather than procedures that should be reduced. Over half of data collected by businesses is often not used, suggesting that time invested in data collection could be improved. The challenges highlighted here suggest companies are still exploring how to utilise information in this new data generation. 

We are still in the early days of data transformation. The success of technology leaders who place data at their core is influencing others to follow a similar path. Data hold considerable value, and businesses are aware of this, as proven by the rise of data-focused AI experts in organisations. Companies need to implement the correct measures, and one important area is focusing on people as much as we are on the actual technology. 

Data can enhance the operations of any function within a business. While emerging technology could provide endless opportunities in the future, the priority today for each business is utilising the data available and ensuring the relevant people have access to this information to make vital decisions. This person doesn’t have to be a data scientist. It could be an engineer looking to explore potential errors in a manufacturing process. All of these people require the data in front of them to continue to generate vital insights. All people can utilise data, especially if a business invests in them and ensure all employees have basic data and analytical skills. In this process, accessibility is the key ingredient. 

Implementing data and analytics enhances the bottom line for any business as long as it includes a clear plan with appropriate measures. The initial step should focus on making data more accessible and simple to use. Creating an all-inclusive data culture is just as critical for a business as the data infrastructure. 

Artificial Intelligence or AI has transformed the financial services industry worldwide. In just a few years, the industry has become more dependent on technology supporting data aggregation, security, products and services. The initial launch of AI technology primarily focused on deriving insights. Since then, the adoption of AI has progressed to other areas of the finance industry, including fraud detection, customer identity and other authentication techniques.

The finance industry is continuing to automate and improve various areas of business with robotics and AI. We are likely to continue seeing a shift towards accelerated machine learning in enhancing human impact across the business. AI and ML have already established a better understanding of customer behaviours and preferences, enabling the finance industry to create a more personalised approach at scale and improve the overall customer experience. Moving forward, this element of AI will become even more important in the finance industry. The ability to provide a more custom and personal customer experience is valuable in finance. AI sits at the core of delivering these features. It can be in the form of personal loan offerings based on a range of parameters. Customers no longer need to select off the shelf products. Instead, individuals have access to unique offerings designed especially for them. AI-powered lending plans is another emerging trend. Portfolio management and retirement planning with the support of AI can deliver intelligent investment plans tailored to each individual.

As the finance landscape continues evolving, we will likely see emerging regulations beyond protecting bank data and other personal information. Having the power to detect trends in large data sets, AI can determine unique information based on data, such as online purchases or website visitors. 

The finance industry has the opportunity to select from a wide range of use cases to determine how they can apply AI to their advantage. They also have the data available to leverage the insights and deliver value for their customers and clients. Incumbent businesses will need to adapt and explore their operations, shift away from legacy processes and harness the real benefits of AI. With AI still evolving, early adopters of this industry will likely gain a significant advantage and the necessary experience to succeed. Businesses that fail to adopt these measures until established will risk falling behind their competitors in the future.

In terms of fintech, AI offers several disruptive opportunities. While these may present a threat to the incumbent banks, there are also many opportunities for traditional banks to partner with fintech. Banks have the added advantage of having an existing large customer base, while fintech has access to new technology and AI features. With the right plan in place, there is a chance to deliver a win-win situation for banks, fintech and the customer. Over the next few years, we will likely see a rise in automated technology interacting with the end-user. With the pandemic showing the importance of remote services, bots and other similar technology will become even more familiar in the finance world. A recent survey by EY discovered that 64% of financial businesses plan to significantly increase the use of AI technology within the next two years. Analysts believe AI will become a vital part of the finance industry, generating new revenue channels and automating processes to enhance the customer experience. No area of the finance sector is likely to remain disconnected from AI in the future.