The financial services industry is adopting artificial intelligence (AI) at an unprecedented pace. And that’s understandable. AI and machine learning are proving to be vital enablers of the digital transformation financial services firms should undertake to realize greater efficiencies, improve the customer experience and revitalize their growth. Now that machine learning is becoming mainstream, there’s a new wave of AI innovation on the horizon: artificial neural networks.

A step-change in the power of AI

Based on multi-layered neuron structures that mimic the human brain, neural networks represent a step-change in the power of AI. Neural networks are extremely complex machine learning algorithms composed of interconnected neuron layers. Reasoning occurs within each layer, but the different layers also interact with and influence each other. This interaction and influence results in a very powerful model for sophisticated decision-making.

Neural networks are widely applicable technology that is very appropriate to the financial services industry, given the availability of mass amounts of data as well as algorithm innovation facilitated by cloud-based computing power. These networks are expected to create opportunities to automate ever more complex processes and decisions with the highest possible degree of accuracy. That’s an exciting proposition for the financial services industry, if firms can take the appropriate approach to adopting these new capabilities.

Neural networks are promising…but should be approached with care

Financial services firms that have executed early AI proofs of concept (PoCs) involving neural networks have garnered promising results. However, these efforts should be approached with care. Why?

Developed in academia, neural networks were designed to deliver the highest possible accuracy with little focus on explainability (the ability of the algorithm to justify its decision). That can be problematic for the financial services industry. It’s essential that in regulated sectors like banking and insurance, where both regulators and customers often want to know why a particular decision was made, firms should be well equipped to explain AI reasoning. The inability to do so could expose firms to significant risks, ranging from legal challenges to a loss of customer trust.

Mitigating risk with the right approach

The good news is that these risks can be avoided with the proper approach to using neural networks. In my next blog post, I’ll explain the pros and cons of neural networks, what can go wrong and how you can avoid neural network pitfalls by putting in place the right processes, practices, tools and controls to make responsible and ethical use of these extremely effective capabilities.

For detailed information on neural networks and how to apply them in financial services, please see Accenture’s report: Neural Networks: The Next Step for Artificial Intelligence in Financial Services

Submit a Comment

Your email address will not be published.