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The AI Disconnect in the Financial Services Industry

Few industries are leveraging AI to the full extent of the technology’s power

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  • Written by  Dr. Akli Adjaoute, CEO of Brighterion
The AI Disconnect in the Financial Services Industry

Artificial intelligence (AI) has seemingly permeated our world. Everything from cars, to phones, to music streaming services claims to include the technology, with more applications promising new AI capabilities almost daily. However the reality is, few industries are leveraging AI to the full extent of the technology’s power.

The financial services industry, in particular, stands to gain incredible value from AI, leading many financial organizations to eagerly work to leverage the technology to more effectively service their customers, manage new and ongoing investments, combat fraud and augment their workforce, however thus far, these AI implementations haven’t been entirely successful.

Brighterion recently surveyed more than 200 financial institutions across the U.S. with assets ranging from $1 billion to more than $100 billion. With a goal of determining how AI and machine learning are being used in the financial services industry, we found that the average bank uses approximately 2.7 different supervised or unsupervised learning systems, including data mining, neural networks, fuzzy logic, and business rules management systems (BRMS). For banks with more than $100 billion in assets, our research found that 100 percent use some form of data mining, 90.9 percent have adopted neural networks, 72.7 percent use fuzzy logic and 54.5 percent are leveraging BRMS. 

AI’s Buzzword Status Dilutes the Technology’s Full Impact

Enthusiastic adoption of AI and machine learning is admirable and welcome, however the trouble with such technology becoming so ubiquitous in an industry as vital as finance is that it reaches buzzword status. True AI technology must include the following three fundamental capabilities:

  1. Ability to personalize: Instead of using static or generic categorizations of profile behaviors from broad groups, effective AI systems recognize the unique, individual behavior of an entity over time. This is a crucial capability for financial organizations looking to successfully protect and serve their customers, employees and any other audiences.
  2. Ability to adapt to new information: True AI technology is data agnostic, works with any data, any format, from any sources and produces results in real-time.
  3. Ability to self-learn: Comprehensive AI systems are able to learn from every activity associated with each specific entity, as well as the behaviors associated with entities over time.

The Real Power of AI Lies in Large-Scale, Hyper-Personalization

Perhaps the most valuable aspect of AI that financial organizations need to prioritize is the technology’s ability to learn and make real-time observations from interactions with human users. Using this knowledge, AI can then create virtual representations of every entity with which they interact, building a digital profile that optimizes customer-facing payments and banking services.

Unlike legacy machine learning systems, which apply the same model logic to a large group, effective AI can focus on individual entities, such as consumers and devices and their unique attributes and behaviors, which enables financial organizations to offer hyper-personalized financial and payments services. AI systems are able to perform this type of analysis at scale and with respect of privacy, allowing flexibility to be configured to fit into the model governance framework of financial organizations.

Additionally, true AI systems can provide financial institutions the ability to collect information on any number of actors in an ecosystem, i.e. not just from consumers, but also from point-of-sale (POS) terminals, merchants and so on. This information can then be leveraged to further “teach” the AI system how to best manage different operations and successfully personalize on a massive scale. For example, if a bank has 200 million cards transacting, there can be 200 million instances of AI analyzing and learning the behavior of each. Decision-making is therefore specific to each cardholder, bank or terminal and no longer relies on logic that’s universally applied to all cardholders, regardless of their individual characteristics. 

A Tremendous Growth and Development Opportunity Awaits

Without a doubt, AI has already taken hold in the financial services industry. In fact, the conversation is no longer about whether financial organizations will or should use learning systems to optimize their businesses, but rather how. To realize the full potential of AI and make the most of such a substantial technological investment, financial institutions must recognize the stark differences between various supervised and unsupervised learning technologies, and carefully consider which functions are best suited to specific business objectives. In doing so, financial organizations of all sizes can tap into the tremendous growth and development opportunities AI systems are capable of providing.

Dr. Akli Adjaoute is the CEO of Brighterion

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