Aunalytics unifies siloed bank customer data with AI-driven data mart and NLP

Aunalytics announced an update to its Daybreak for Financial Services platform that employs machine learning algorithms to enable midrange banks and credit unions to more easily analyze data.

The latest update adds a data mart that automatically discovers and aggregates customer data residing in siloed lending, mobile banking, automated teller machine (ATM), customer relationship management (CRM), wealth management, and trust applications. The platform has also added support for a natural language processing (NLP) engine that eliminates the need to know SQL to query data. Companies can automatically create visualizations of those query results as well.

Finally, Aunalytics made it simpler to access external data via connectors and added a “smart features” capability that will, for example, automatically generate alerts anytime a customer’s credit score changes.

Midrange banks and credit unions are at a distinct AI disadvantage compared to larger financial services rivals that can afford to build their own AI models with specialists who know how to program in Python or R programming languages, Aunalytics president Rich Carlton said. “They can’t afford to hire a team of data scientists,” he added.

Aunalytics is making a case for a platform that automates low-level data science tasks in a way that enables either end users or a small team of data scientists to maximize the value of the data any midrange bank or credit union routinely collects, Carlton said. The Daybreak for Financial Services platform is based on cloud-native technologies such as Hadoop, containers, and Kubernetes clusters that he said enable it to be deployed in the cloud or an on-premises IT environment.

Midrange financial services providers have realized they are losing touch with customers in the wake of the COVID-19 pandemic as they rely more on digital services. The number of banking customers that visit their local bank has sharply declined as reliance on web and mobile applications increases. The challenge midrange financial services face today is that they already rely on a disjointed suite of applications to manage their business. Mobile applications in particular have added yet another silo that makes it difficult for financial services providers to correlate customer activity across a portfolio of services.

Awareness of AI and data science has never been higher. The issue organizations are trying to come to terms with is to what degree they are now at a competitive disadvantage because they lack these capabilities. Platforms and applications that embed AI capabilities may provide a way to close that gap at a time when many smaller financial services firms need to operate as efficiently as possible just to stay afloat.

As data science and AI continue to evolve, organizations will soon need to decide when it makes sense to employ advanced analytics that are baked into a platform such as Daybreak for Financial Services versus building and maintaining their own AI models. Given the general shortage of data science professionals, it’s especially difficult for smaller organizations to hire and retain in-house talent.

At the same time, it usually takes a data science team several months to successfully deploy an AI model in a production environment. Providers of applications and platforms may very well have added similar capabilities to their offerings before that custom AI project ever comes to fruition. In many cases, organizations will find they are gaining access to advanced analytics capabilities at no extra cost as new updates are made available under a subscription license.

Most end users, of course, are a lot more interested in the business outcomes AI models and data science enable than they are in the processes employed to build them. The fact that an independent provider of a platform or application is willing to vouch for the accuracy of those AI models adds yet another perceived level of comfort.

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