AI for Financial Institutions

Beyond the Hype

Recently, we attended CXFS (Customer Experience for Financial Services Event). It was interesting how the discussions circled around the topic of artificial intelligence (AI) for customer experience (CX) in financial services with much enthusiasm, but overall the discussion never moved beyond the conceptual. In this article, I aim to bring the discussion down to earth by looking at what are the benefits of using today’s available AI technology in CX as applied to financial services.

Challenges of CX in Financial Institutions

First, let’s explore challenges financial institutions face when it comes to CX. I’ve extracted the most often mentioned challenges at the CXFS by the participating companies.

  • Measuring performance & optimizing the customer experience across different touch points, business units and locations;
  • Identifying pain points quickly, before the problem escalates;
  • A fear of missing out, disruption or blind spots;
  • Personalizing services for individual needs at scale;
  • There is plenty of available data, but extracting stories is not accessible;
  • Aligning siloed departments to adapt to the CX mindset;
  • The desire to innovate trips over cultural roadblocks.

AI, The Solution to CX Problems

All the challenges above can be addressed by our existing technology. These are real problems, that can be solved right now using legacy data and a smart AI system that makes sense of it.

Measuring performance & optimizing the customer experience across different touch points, business units, and locations

Aggregating data from several sources in one system will help construct a macro view. Furthermore, our system finds correlations automatically between unstructured data and structured metadata. This means that if a location (real or virtual) is underperforming it will be highlighted automatically by the system. Also, trends can show how customer experience has improved as a result of actions taken.

Identifying pain points quickly, before the problem escalates

Are people complaining about a new issue? How fast can you know about it? Real-time should be the goal… Uploading data on an ongoing basis is easy with connectors with existing systems such as Salesforce. The AI system is able to highlight when a topic has been identified to show up at a higher than the statistical normal frequency. This allows decision makers to act quickly and take action before the situation escalates into a crisis.

A fear of missing out, disruption or blind spots

Like any mature industry dominated by large companies and institutions, executives are being kept up at night by one possibility: something so profound will change that we will no longer be relevant. Is it possible to see change coming? Are there early signs? All major disruptions started with customers not being satisfied with the status quo. Another company was there to fix the problem and make lots of money. Leveraging the power of deep learning to close the loop on customer feedback means that your organization will not be left behind. Choosing the right technology is key. Most text analytics solutions rely on keywords entered by humans to monitor ingested text. Keatext algorithms are able to extract meaning from the data, without having to rely on human intervention. Our blindspot-proof technology ensures our customers know everything that’s happening in real time.

Personalizing services for individual needs at scale

Imagine the world where society and financial institutions evolve as one. This would require re-establishing that intimate connection that was once achieved by human contact: local bank, with a manager that has known individual customers for years, lovingly offering bespoke financial products for their customers, updating services and offering trustworthy counsel. But in the era of digital transformation, technology is taking that personal touch away. The solution? A new breed of segmentation. Not one based on traditional demographic data, but based on patterns. Extracting statistically significant insights from qualitative data means that customers’ can be categorized based on their intent, objections, and context. This is the next level of sentiment analysis technology.

There is plenty of available data, but extracting stories is not accessible

Traditional text analytics solutions are somewhat intimidating: the implementation requires months, regular updates are required, consultants need to be involved, the price tag contains a few too many zeros. Even after the platform is up and running, the interface requires coding meaning only specialists will be able to access it directly. All these challenges are a thing of the past. Our solution lives in the cloud, accessible via any browser with privacy settings that passed the highest level of privacy and security scrutiny. The interface is friendly & intuitive point & click meaning non-specialists can explore the data. Who best to find the right insights than the people who understand the bigger strategic picture?


Aligning siloed departments to adapt to the CX mindset

One thing was clear at CXFS: in order to achieve the best customer experience, multiple departments need to coordinate tightly. However, organizations still have problems with information being siloed. An aggregated system that combines data from all sources will encourage a culture of shared data ownership. Furthermore, results are easy to share with decision makers, thus empowering users to make compelling business cases fast. An organization that achieves this level of agility will blow their competition out of the water.


The desire to innovate trips over cultural roadblocks

The best CX specialists say you can’t just expect technology to fix all your problems. They are right, we’ve seen that with our customers. The internal culture needs to be ready or at least willing to change and powerful internal ambassadors need to promote the use case. However, there are some roadblocks that technology can remove. Seeing demos with internal data can create enthusiasm that will earn vital buy-in. See for yourself a demo with your own data.

What does tomorrow hold?

To get a clear idea of what tomorrow holds, why not check out the post written by Dr. Narjès Boufaden, CEO of Keatext and text analytics expert in conversational language. Who better to foretell the future than those building it?

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