Operational efficiency is a priority for corporate leaders, with AI gaining accelerated traction across industries. While AI has shown multiple benefits and shown significant results, those in regulated industries like finance, are raising specific questions concerning security, data validity and general ethics surrounding AI, especially regarding data privacy. Whether a finance-focused business is applying AI to improve contract management, provide a better customer experience or improve fraud detection, the measures controlling how data is used and retained remain critical.

Colby Mangonon, Associate General Counsel at Evisort believes that financial, legal, IT and operations teams should assess the appropriate data privacy regulations when planning to integrate AI to ensure they are compliant and avoid any potential issues with customers, stakeholders or regulatory groups. Mangonon believes that finance businesses should ensure integrating AI is protected by a structured information security framework and data processing policies to safeguard customer data.

Applying AI to finance

As finance businesses develop their technology stacks to improve efficiencies, many have reinforced their technology with AI-focused solutions to enhance the results of their operations. Some finance companies have started applying OpenAI’s GPT products to enable advisors to deliver vital research and data. For example, a payment processing leader is leveraging AI to differentiate between authentic and mistaken fraud detection and avoid card declines. Another financial business is applying AI to deliver customised contracts and digitally work with internal stakeholders to meet all requirements. AI also delivers multiple opportunities to enhance revenue-focused operations, like accelerating customer services like loan processing or onboarding.

Aside from the benefits, the legal aspect within finance businesses is very aware of the challenges AI presents with privacy and security for customers, stakeholders and business data.

Concerns over Artificial Intelligence (AI)

While AI-driven technology can be very useful for daily operations, there are some concerns about the specifics of data at the business level. Some questions are more specific when exploring generative AI models. For example, ChatGPT has already received added scrutinisation while governing bodies explore the potential legal implications of applying this technology. For example, Italy recently banned the platform, with other EU nations raising concerns about how AI-related data tools meet the standards of GDPR.

Aside from the regulatory concerns, several financial businesses are cautious of using public third-party AI tools for fear that data could be exposed. Leading organisations like Goldman Sachs Group and JP Morgan have temporarily banned using ChatGPT for business communications while they determine a safe and effective way to apply these technologies.

These concerns don’t mean we should avoid AI to protect data in finance, but it means in legal teams must carefully consider each solution to ensure they meet regulatory standards for data privacy and security. Protecting your business when applying AI requires a deep understanding of the specifics and the parameters used to build their technology. Finance leaders should determine the specific data planning to implement into an AI model, as this plays a critical role in selecting the best platform for your organisation.

When exploring business solutions, businesses should consider the following: what are the AI data training practices used, what are the security frameworks and measures, and is the provider using a custom AI model or a third party?

Being careful in examining AI solutions, finance leaders can leverage all the benefits of AI and continue to remain compliant with regulatory standards while eliminating the risk of compromising data.

As AI continues to gain traction in the enterprise tech landscape, legal teams within finance companies will be responsible for meeting data privacy standards and allowing businesses to enhance their operations. With this mindset and a structured approach, businesses can deliver better outcomes and create a competitive edge.

Climate change can transform our global economy, but the finance industry continues to fall behind in terms of implementing the necessary measures to tackle climate change. Representatives at the conference on climate finance in New York stated that to enhance the rate of progress, the industry must take advantage of new technology, particularly artificial intelligence and machine learning.

Discussions on the progression of data and AI, particularly the advancements in gathering satellite-based data, highlighted the information collected in our world. When examining this data via tools such as machine learning, we can explore multiple trends and patterns on our planet. The high-quality data can enable global markets to assess the short and long-term climate risks and opportunities, from understanding the value of the services nature provides to determining what areas influence the climate resilience of investment plans.

How does AI support the finance industry and understands the value of nature?

Some industry experts believe the finance industry currently fails to acknowledge the value of the ecosystem before certain parts convert into assets. In today’s world, we have organisations with clear economic values, whereas nature has no intrinsic economic value until the trees are cut down and converted into productive assets. Businesses can harness data which recognises these critical parts of nature i.e. the water, carbon, biodiversity and other associated services. These variables can then be integrated into the finance industry and future strategic plans.

How to incorporate these values into future financial plans

This critical information can enable a range of decisions for finance-related businesses. Companies can, for example, identify methane emissions detection down to a specific facility in real-time. This information can determine certain facilities that are impacting the environment. Investors and other interested groups can see the level of methane emissions coming from their investments. If that figure exceeds an emission target, the investor may need to sell off an asset or encourage the facility manager to reduce emissions. If it is affordable to detect these emissions, we can make it very expensive to ignore this data.

In contrast, being capable of determining areas or facilities that are doing a positive job in terms of carbon reductions can trigger higher investments. A bank may decide on additional financing for a business with a lower carbon footprint due to lower legal liability risk and enhanced public perception.

Combining two of the most powerful industries, technology and finance, could transform the level of progress on climate change. Delivering significant transparency in global capital markets has considerable potential for bother industries and how they decide to approach climate action. 

Today is the time for tech and finance to collaborate and deliver the necessary changes. The challenges are severe, but we are equipped with better tools and continue innovating with new technologies.

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.

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. 

The Fintech industry has generated significant changes, disrupting many industries, particularly the financial sector. Fintech has created several benefits in finance, improving payments processing, insurance and money lending. The rise of fintech has provided a unique customer experience and enabled people to embrace the transition to fintech.

The majority of fintech customers tend to be choosing traditional financial institutions as there are several challenges that fintech needs to tackle to continue this technological revolution. Some of the main areas to focus on relating to trust, transparency, security and customer trends. 

Security and User Privacy

Across Europe, the use of financial technology increased by over 70% during 2020, supported by considerable investments into fintech. A rise like this comes with new challenges, one of which is developing new security concerns. Cybersecurity cases are rising, and unfortunately, fintech businesses are a prime target for cyberattacks. The fintech industry holds significant valuable information that needs to be protected.

Maintaining a grasp of new technology

According to a recent survey by Gartner, over 50% of financial services CIOs believe that most businesses will work with digital technology and that these channels will yield higher revenue and value. This statement emphasises the importance of fintech on the future of business performance.

Businesses that rely on traditional management systems will not keep the competitive edge needed to maintain momentum with the shift towards digital technologies. Many companies consider the transition to digital as a necessity rather than just being a good idea.

Emerging technologies such as cloud computing, AI, ML and big data offer several benefits for businesses looking to reduce overheads. They also provide the potential to improve the overall user experience. The move to these technologies, however, does come with initial costs and some risks.

AI provides a considerable competitive advantage by creating deeper insights into customer behaviours, enabling financial businesses to assign the right product to the right customer at the right time.

The Quality of Software

Finance businesses that apply the latest business technology create an advantage in the journey towards digital. The ability of new cloud technologies depends on flexibility and scalability. Having flexibility means cloud technology can enable systems to evolve alongside a business. Successful fintech businesses are dependent on reliable IT technology resources.

Industry Regulations

Regulatory compliance has become a challenge within the finance sector due mainly to the rise in regulatory fees attached to earnings and credit losses. There is a growing number of regulations that financial businesses must comply with, and compliance can present added pressures on resources.

The future of the fintech industry is relatively clear. Financial technology is going to have a significant influence on the finance industry. Developing a functional financial solution will require considering all of the challenges mentioned.

CFOs and other financial professionals are fairly optimistic that we are approaching an economic recovery. Over 70% anticipate that we will return to normal by the end of the year, according to the latest Enterprise Financial Decision-Making report from OneStream, a leading provider of corporate performance management solutions. The report suggests that businesses have increased their investment into data analysis tools and general usage considerably over the last year.

The study by OneStream explored financial leaders across North America and determined certain factors that influenced their priorities, budgets and technology integration plans for this year. The findings suggested that the pandemic had generated a bigger need for flexible forecasting, predictive planning and digital transformation. The capability to forecast budgets quickly and change workflows have become a critical feature since the pandemic began.

The report also discovered that financial executives have significantly increased their usage and investment in data analysis tools. Businesses generally tend to invest in artificial intelligence and the results showed increased use of cloud-based planning and reporting solutions. Most businesses (around 70%) are already using or plan to adopt (20%) a low-code development platform that allows business users and developers to adopt new roles while managing complex coding requirements.

In the case of return-to-office budgets, data privacy tools are regarded as the most common priority, followed by hybrid cloud solutions.
In comparison, the OneStream 2020 Enterprise Financial Decision-Making Report indicated that less than half of finance executives used cloud-based solutions regularly for reporting. The 2020 report also showed that less than a quarter used machine learning and artificial intelligence solutions.

Many financial leaders are exploring their workforce, technology and supply chain requirements in a post-pandemic world. However, the influence of changing policies and social development has had a significant impact on investment plans, encouraging leaders to focus on sustainability and diversity measures as well.

The study was conducted by Hanover Research this year covering hundreds of financial decision-makers in Northern America. The full OneStream Report – Enterprise Financial Decision-Making 2021 – North America can be viewed here.

Forward-thinking financial businesses are applying data, analytics and AI to create new products and services and to enable the delivery of more intuitive and personalised service. Future success requires attracting skilled talent, enhancing existing processes and meeting the increasing demands of customers.

The finance industry is continuously focused on digital transformation and utilising data, analytics and artificial intelligence to improve their services. Banks are applying AI to enhance their current offering, build new products and improve the overall customer service. Data and analytics are proving vital in supporting the progression of new business models and improving decision making.

This significant shift comes with its challenges, such as accessing data, the quality of information, the available skilled talent and the delivery of necessary resources to generate the required results. Most financial teams also lack the required digital experience to create and build a digital-focused organisation. Nevertheless, the decision-making influence of data and analytics provide financial businesses with the competitive edge needed to provide the added value to their customers.

According to studies from McKinsey, Financial businesses that apply data and advanced analytics to engage with customers and drive new business are capable of generating value in several ways:

Enhanced Customer Acquisition: By focusing on delivering personalised information at every step of the customer acquisition journey, engagement becomes simplified and the overall customer experience is improved.

Higher Customer Lifetime Value – Offering new products and services to customers is done through intelligent communication and recommendations based on real-time analytics and data.

Reduced Operational Costs – Automating and simplifying data processes and decision making plans, overall efficiencies are improved and costs decline.

Reduced Risk – Applying advanced analytics enables selective decisions on potential customers and allows detailed analysis of customer behaviours to detect potential cases of risk and fraud.

Financial businesses are increasingly looking for analytical solutions to improve their position against other competitors. According to a report by the Cambridge Centre for Alternative Finance and the World Economic Forum, several global financial services firms have implemented or are working towards adding AI solutions within a range of business functions. Data from the World Economic Forum suggests that approximately 85% of all financial businesses have implemented AI, in some shape or form.

Beyond the security and risk detection side, an increasing number of financial services are applying analytics to create new services and an innovative customer experience. Advanced analytics can be applied across the entire customer journey, from initial engagement through to evaluation and ongoing relationships. As a business uses AI to further deepen its understanding of each customer journey, the organisation can deliver highly personalised offerings directly to the customer in real-time.

Applying data to generate insights that can be applied across the business isn’t enough. Financial businesses that engage continue to increase, experiences are enhanced and loyalty is strengthened. In other words, the ROI on data and AI is not fulfilled until these factors are achieved.

The financial industry is experiencing a significant shift driven by new technologies. Many business leaders are continuously looking for techniques to improve their performance and introduce new, efficient solutions. With the rise of new technology comes new challenges for the finance industry.

Being prepared for cyberattacks

Financial businesses are particularly vulnerable to cyber attacks due to containing large volumes of very sensitive customer information. As one report shows, financial businesses are generally liable to cyberattacks more so than other industries. Compromised credit cards, leaked information or malicious banking processes have forced businesses to utilise and embrace new technologies and protect their organisation from very expensive challenges. 

By implementing advanced technologies businesses can greatly improve their security measures and greatly reduce potential cyber-attacks and the associated expenses. Security represents a top priority for financial businesses due to the accelerated rise of professional ‘attacks’ in the last couple of years. Business can utilise new verification services, a fraud prevention system that verifies user information. End-to-end encryption enables no external group to access certain sensitive information. 

Maintaining a connection with new technology

Recent studies suggest that financial businesses need to continue investing in robotics and other automation tools to enhance their effectiveness and reduce costs linked with operational processes, risk management and compliance. Businesses need to upgrade their internal systems and data facilities to utilise the benefits of big data solutions, such as AI-focused support assistants. 

Exploring robotics can allow financial industry businesses to replace traditional, manual services with automated processes, improving productivity, accuracy and compliance. Businesses need to be prepared to embrace new technologies such as robotics and artificial intelligence, machine learning and NLP.

Keeping your business compliant

REgulations, compliance and laws are a constant challenge for the financial industry. The pressure to remain authorised and compliant relates specifically to the rising regulation fees that emerged from the global financial crisis back in 2008. Today, multiple regulations have driven financial businesses to streamline their processes.

Implementing regulatory technology to stay compliant enables businesses an efficient management and risk assessment process for organisations. RegTech is generating added value for a company seeking to streamline processes associated with regulatory compliance. RegTech businesses provide ‘know your client’ and anti-fraud services, tax data management services, real-time reporting and regulatory compliance assessment tools.

The challenge of meeting customer expectations

Today’s customer is tech-savvy and generally expects high-level custom features within their banking services. Younger customers typically have a better understanding of technology and as a consequence have higher expectations of their digital experience.

Generation Y, people that fall between the ages of 22 to 38 are responsible for nearly 50% of mobile banking users and have the biggest impact on the digitisation of financial services. To be capable of meeting the needs of both the older and younger customers simultaneously, financial organisations need to create a hybrid banking model that combines digital experiences into a traditional banking environment.

Financial businesses can continue to succeed and have a considerable advantage over their competitors if they continue to embrace digital technology. With a combination of AI, robotics and regulatory technology, businesses can continue to innovate and manage the challenges faced in finance, while continuing to remain compliant and keep progressing.

A final factor is maintaining a close consideration of customer satisfaction. To maintain the highest level of customer satisfaction, financial businesses need to incorporate a blend of traditional and digital banking methods. The combination of the solutions described will enable finance businesses to reach their future goals.

Artificial Intelligence (AI) is progressing at a rapid rate and advancing systems are becoming more efficient at adapting to new scenarios and performing in ways that are similar to the human brain. With further technological advancements, there is great potential for AI to transform large businesses in the coming years. Software-as-a-service (SaaS) tools have had a significant impact on how businesses operate and perform today. Artificial Intelligence will potentially reshape the next wave of this software, enabling companies to not just collaborate on innovative software, but also to gain a deeper insight and share further information and knowledge.

This generational transition means digitally-focused individuals are becoming more dominant in the workplace. For these employees, support from AI will become a regular part of workplace activities, providing additional support and enabling people to learn new skills. The businesses that implement intelligent use of AI will likely have a greater chance of attracting and retaining employees for longer. Industry experts believe AI strategies will be an essential part of business competitiveness.

The concerns surrounding the rise of Artificial Intelligence

The common concerns related to how AI could eliminate jobs across multiple sectors, to issues about the potential threat of creating an uber-intelligent system and its impact on our planet. For every investor in artificial intelligence, it is critical to ensure we remain focused on using AI as a technology to support humans and not to replace them.

AI has the potential to empower people, to increase the sharing of skills and knowledge. Revealing the insights with AI from thousands of human brains could dramatically enhance intelligence levels. For business leaders, it is clear that AI has a positive impact on productivity and efficiency, increased workplace happiness and improved workforce retention.

The potential challenge for the future is ensuring businesses utilise AI in a positive manner. Aside from supporting human knowledge and skills, AI could be implemented to measure employee performance and identify employees underperforming. If businesses implement AI in this manner may eradicate trust within a business. When considering how AI can be used, businesses should carefully consider the impact new technology will have on trust and reputation.

The rise of data and innovative workplace software systems mean businesses have significantly higher access to information than ever before, however, a large proportion of data and information remains stored in our minds. Businesses and innovative solutions that are capable of revealing this information are likely to rise efficiency levels significantly. Moving forward, businesses will need products and services that let employees discover and share skills and knowledge and Artificial intelligence will be vital to unleashing business productivity for the future.

In a recent report, Melissa Boxer, the VP of Oracle explains how Oracle Adaptive Intelligent Applications can remove the repetitive and tedious ERP and supply chain jobs from people’s work.

Over the last few years, Oracle has increased its investment into implementing AI and Machine Learning capabilities within a range of their applications, in particular, the Oracle Adaptive Intelligent Applications. Boxer explains that its Adaptive Intelligent Apps are focused on creating next-generation smart innovative applications based on big data generated within the Oracle Cloud. Boxer highlights that AI and ML can be integrated into Oracle Cloud-based applications in various methods. She explains how the automation of tedious and repetitive jobs such as invoice matching or approving expenses are tasks that can be handled by AI/ML tools within the cloud today.

Boxer explains that ML can manage specific users cases within conventional business activities, such as procurement and accounts payable. Oracle then expand on this potential by using Oracle’s ML algorithms to enhance the entire process.

For Oracle, ML requires accurate and relevant data to generate the best results. It’s quite clear that relying on poor data for ML models will mean poor quality results. Data fed to the Oracle Adaptive Intelligent Apps utilises a blend of first and third party data pools. Oracle uses tuning processes, using training data and ML algorithms ensuring customers receive the benefits of instant value when the application is activated. The data records are consistently added to the Oracle ML learning systems, enabling models to be constantly refined based on customer data. The concept behind these models is to reduce the reliance on having a pool of data scientists, which for many businesses is simply not economically viable.

Boxer points out that there is no assumption that the AI developed for standard projects will work for all. The system needs to gain an understanding of the customer business, which involves learning from customer actions and their response to recommendations. Through each recommendation, the system learns from the customer response. The information generated supports ML algorithms and ensuring they are continuously improved.

For Oracle, AI is integrated into applications that existing customers use, meaning they don’t have to become familiar with another platform. Chabot engagement is a growing system that is developed to provide an enhanced mobile experience and enable people to perform a task by responding to several simple questions. In terms of expense reporting, this applied process can save thousands of hours for both the employee and managers, who presumably have better things to be doing than collating expense reports.