AI, ML, and Data Proliferation in Financial Services
AI is gaining popularity in applications such as risk assessment, detection of fraud, and planning sales and operations.
- Financial organizations have been undergoing major business models and operations changes recently. While profitability continues to be the leading priority, inflation, natural disasters, and changes in interest rates have challenged them.
- Artificial intelligence, machine learning, and data management are expected to continue gaining momentum as the risk-averse financial sector looks to deal with large-scale changes.
Finance organizations are extremely dependent on data, and while raw data is available in more abundant volumes than ever before, frequent changes in market conditions have resulted in a lower probability of security, with a growing concern about factors such as inflation, natural disasters, and national interest rates.
In such a situation, this conventionally risk-averse industry is rapidly turning toward systems and tools that offer superior artificial intelligence, machine learning, and data management capabilities so that they can improve how they react to major changes in a timely manner without compromising security and operations.
Why Financial Organizations Are Leveraging Emerging Tech
Firms providing financial services have witnessed an uptick in profitability following the slump that followed the pandemic. According to Aberdeen Strategy & Research, the financial services sector not related to insurance has achieved at least 15% net margins since 2017, as seen below.
Source: Aberdeen Strategy & Research
While this can be viewed as positive, global issues such as shortages in raw materials, environmental disasters, and disruptions to supply chains have given rise to continued inflation over a longer-than-expected period. This, in turn, affects factors such as purchases and credit rates, raising costs.
As seen below, interest rates have risen strongly, primarily driven by inflation, essentially adding volatility to an increasingly precarious situation for a risk-averse industry.
Rising Interest Rates in the U.S.
Source: Aberdeen Strategy & Research
This complexity makes it critical for financial institutions to optimize systems for forecasts, analysis, and planning day-to-day operations. Data management, AI, and ML are increasingly being viewed as excellent tools in this regard.
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Key Stresses and Potential Solutions
Large-scale financial organizations often struggle to gain a complete picture of their operations, especially due to activities like mergers and acquisitions and the resultant decentralized nature of the overall entity. Varying systems and data models make getting a unified view of day-to-day operations difficult.
Furthermore, this makes it difficult for an organization to understand its shortcomings and set the best objectives for itself. Depending on the type of business, the consumers, and the location of operations, every organization has different stresses that they identify as their leading priority.
Leading Stresses for Financial Organizations
Source: Aberdeen Strategy & Research
According to Aberdeen Strategy & Research, perception of the organization in the market, followed by the maintenance and security of data, were key concerns for approximately 30% of financial organizations. Other key concerns included the complexity and volumes of information, the lack of skilled professionals, and disruptions to supply chains.
In such cases, efforts are largely focused on customer service and retention, in addition to the modernization of applications, infrastructure, and technology. Once again, the cloud seems enticing to support such requirements.
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A key challenge is associated with the method of deployment. According to Aberdeen Strategy & Research, more than 38% of financial firms use more than one system for operations, including cloud, hosted services, and others. More than 10% use over twenty versions of such systems, making data proliferation a major challenge.
Consequently, implementing best practices is a significant effort, and data access and sharing is difficult. However, in all cases, deployment of such solutions has largely resulted in significant improvements to operations, as can be seen in the following table:
Source: Aberdeen Strategy & Research
Firms that invested in such deployments have reported better performance in terms of profitability, faster times in decision-making, and return on investments.
Operational Improvements per Deployment Method
Source: Aberdeen Strategy & Research
Cloud deployments have displayed better outcomes than hosted deployments and other alternatives. This can be attributed to consolidation and centralization of systems, which cuts data proliferation challenges.
Artificial Intelligence Gains Attention
According to a survey by Aberdeen Strategy and Research, efficiency and customer relations, two of the greatest priorities for financial firms, had seen significant improvement of more than 50% due to adopting AI solutions. AI has also found significant favor in cybersecurity for sensitive financial data, and this factor is also expected to drive adoption in the sector for the foreseeable future.
According to the research, data analytics is the primary application of artificial intelligence for operations such as planning operations and sales, fraud detection, and risk assessment. The primary application of AI, however, is related to business analytics, including descriptive, predictive, and prescriptive analytics, which is also driven by the disparate systems and tools used to handle data.
Best practices associated with the deployment of AI, ML, and data management include:
- Clearly setting out goals and using those to choose appropriate solutions.
- Looking for technical services and online support from vendors.
- Keeping an eye on emerging trends and technological developments to gain a first-mover advantage and minimize risks.
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Firms that provide financial services are expected to face challenges in terms of frequent changes to technology and market conditions. The number of disparate systems and tools for myriad applications adds to the complexity of operations. Consequently, the use of cloud-based systems could emerge as a partial solution. Coupled with artificial intelligence and machine learning, organizations can better customize their arsenal to reach desired outcomes.
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