- The platform enables support for natural language processing engines which provides the elimination of the need to know the SQL for data query.
- With added smart features and capabilities the external data will be easier to access.
With the new update, it employs machine learning algorithms to enable midrange banks and credit unions to easily analyze the data. The recent update has added on data mart which automatically discovers and aggregates customer data stored in siloed landing, mobile banking, automatic teller machine, customer relationship management, wealth management, and trust applications. The platform has also enabled support for a natural language processing engine that provides the elimination of the requirement to know the SQL for data query. The companies can automatically develop visualizations of those query results also.
Finally, the company aims to make it simpler to access external data through connectors and also added smart feature capabilities. These capabilities generate alerts anytime a customer’s credit score changes. Mid-range banks and credit unions are at an AI disadvantage compared to larger financial service arrivals who can effort to develop their own AI models with specialists in Python or R programming languages. Aunalytics president Rich Carlton says, “They can’t afford to hire a team of data scientists.”
Low-Level Data Automation:
Aunalytics is developing a case for a platform that provides automation of low-level data science tasks in a way that enables end-users for the team of data scientists to maximize the value of data in any mid-range bank or credit union routinely collects, added Carlton. The Daybreak for Financial Services platform is based on cloud-native technologies which include Hadoop containers and Kubernetes clusters that enable the deployment in the cloud or an on-premises IT environment.
Mid-range financial services providers realized that they have lost touch with customers because of the pandemic as they started relying more on digital services. The number of banking customers who visit their local bank has seen a significant decline as they have started relying heavily on web and mobile applications. For mid-range financial services, the challenge is that they have already been relying on a disjointed suite of applications for their business management. The addition of mobile applications has added another layer of isolation which makes it challenging for financial services providers to correlate with the customer activity across a portfolio of services.
The issue organizations I trying to understand is to what degree they are now at a competitive disadvantage since they lack these capabilities. Platforms and applications which embed AI capabilities may be able to bridge the gap at a time when a lot of smaller financial services firms need to run their operations efficiently just to keep floating.
AI Models for Financial Services:
As data science and AI continue to evolve the organizations will have to decide when they should employ advanced analytics baked into platform Daybreak for Financial Services vs developing and maintaining their own AI models. Taking into consideration the general shortage of data science professionals, it is specifically difficult for smaller organizations to hire and retain in-house teams.
At the same time, it takes a data science team a couple of months for a successful deployment of an AI model in a production environment. The application providers and platforms may have added similar capabilities before the custom AI project ever came to fruition. In many scenarios, organizations will find that they are able to gain access to advanced analytics capabilities at no extra cost. Moreover, fresh updates are available with the subscription license.
Most users are interested in business outcomes that AI models and data science bring about rather than in the process employed to build them. The fact that an independent provider of a platform or application is willing to take accountability for the accuracy of AI models at another level of comfort.
