The current global business intelligence market is strong with total growth forecasts to expand from USD $23.1 billion in 2020 to USD $33.3 billion by 2025, at an analyst predicted compound annual growth rate (CAGR) of 7.6%. This growth is being driven by several factors — increased focus on digital transformation, rising investments in data generation and analytics, growing needs for data visualization dashboards, and an industry-wide shift to managed services and the cloud. The implementation of advanced data analytics improves the efficiency and productivity of business operations and strengthens the organizational workforce. The techniques used for the preparation of data platforms prior to implementing data analytics help to identify and fix the errors in data sets with the employment of data management and filtration tools, which further improve the quality of data to benefit both consumers and institutions, including banks, credit unions and other financial service providers.
As the financial services industry is one of the top consumers of business intelligence and analytics, these organizations are on the hunt for cutting-edge technology, such as universal cloud-native data management and predictive analytics, in their mission to identify customer preferences with regard to financial products as well as the need to better understand operational systems and conditions throughout the business. Throughout the industry, IT leaders are adopting ever more advanced analytics and AI/ML solutions by harnessing the data generated both inside and outside of their organizations in a way that delivers actionable business insights. Data platform and predictive analytics solution providers are the primary enablers behind many next-generation initiatives and are managing the lion’s share of this work for community banks and credit unions by extracting strategic insights, prioritizing market share, expanding products/services, and monitoring key performance indicators (KPIs) to maintain operational excellence. This supports community financial institutions to better compete against national banks by strengthening the level of decision making, while empowering white glove service with powerful data analytics.
In the case of one mid-size bank holding over USD $10 billion in assets, there was a deficit of business intelligence impacting revenue numbers, customer service levels and other operational areas. In conjunction with an industry provider of predictive analytics and artificial intelligence (AI) solutions, the bank IT leadership was able to aggregate disparate data into a single cloud platform that was leveraged for unprecedented AI-based insights used to answer questions leading to measurable business outcomes for enhanced customer service and greater revenues. The technology behind these positive outcomes includes the use of automated data management, industry-specific data marts and AI powered Smart FeaturesTM as a service, capable of culling through information in ways that human resources could not physically accomplish in order to provide critical daily intelligence.
Smart Features in particular, are exceptionally valuable as they allow for deep customer intelligence that is defined as high-value data attributes or fields present within advanced data models; identified by AI powered algorithms built to spot trends and characteristics of a customer, and appended to the customer data model to enhance a customerās 360 view with insights gleaned from the mined data. The ability to compose a Smart Feature within the data platform provides higher scalability for the data models and more extensive machine learning (as opposed to creating views of the data after delivery to a visualization tool commonly used in the industry). This provides high-value capabilities for companies dealing with difficult to compute data caused by the scale of the data sets fed into analytics, support for production cadence or algorithmic complexity.
Smart Features also allow for direct or indirect comparisons across clients within the same industry vertical, providing useful benchmarking and overall calibration. In other instances, it brings global data to enrich the data pool beyond natively available information within the respective enterprise. The features enable the extraction of semi-structured and unstructured data, as well as the interaction with content such as plaintext, clickstream, IoT, video and audio data, which can be incredibly valuable. Finally, Smart Features employ machine learning to support future predictions and enhanced data classification by generating data fields withing the platform. The problem of including transactional bank data in analytics is solved by using a data platform with automated ingestion that includes change data capture and streaming capabilities for real-time data integration, built-in data management and cleansing to prepare an analytics-ready data stream. This rich and vast transactional dataset, which is typically too complex and time consuming to use in a deep learning model tasked with providing up to the minute analytic results, may be mined to provide timely insights for competitive advantage.
In the financial services space, this higher grade of insight involves the integration of data from multiple sources that includes banking Core data, CRM data, third-party loan and credit card systems data, mobile banking, marketing, and the like. Based upon the mining of this data, including hard to tackle transactional data, semi-structured and unstructured data, AI powered Smart Features can be enabled to add knowledge and context about financial services customers. For example,a Smart Feature may yield customer intelligence, revealing such things as external banking relationships held by customers and giving financial institutions a deeper view of client transactions beyond just the client institution. This yields warm leads for expanding business with the customer.
Rather than marketing a product to all customers, the financial services establishment now has the option of targeting customers most likely to purchase the product. Such targeting may include qualifying criteria for a product, such as a minimum credit score and/or recognizing its most valuable customers by the enhanced data models, then targeting individuals for future business. In this manner, marketing becomes data-driven, targeted, more personal and more efficient. It allows the institution to target the right customer at the right time and use its personalized white glove service to win business over mega-banks vying for customers.
The benefits of predictive analytics and AI also serve the needs of credit unions who recognize the importance of data-driven business intelligence. At a premier Midwest-based credit union concerned about increasing competition in its areas of operation, the organization recognized the need to centralize and leverage the insights of member data. Like many credit unions however, the data about members was scattered across many financial systems, including its Core system, lending systems, mobile banking systems, third-party credit card systems, Jack Henry systems and more. In order to solve this challenge of distributed data resources, a cloud-native analytical database with built-in automated data management and intelligent connectivity to integrate third-party data sources was deployed.
After the data integration was complete, the credit union’s newly minted platform was then able to normalize and cleanse data to ensure quality and enable strategies and tactics based on an advanced data purification process. Afterward, the data becomes available for machine learning activities, which in this case include model development and the generation of predictions and classifications based on historically observed information. This was utilized for supervised learning, leveraging this more useful and powerful data.
Although this particular deployment has only recently been put into place, the credit union’s time and effort invested in its advanced data platform have already begun to deliver measurable results. The solution does the heavy technical lifting to generate trusted data pulled from its distributed information silos to provide the answers the organization has been searching for to make their member experiences even more meaningful.
As presented here, a universal cloud-native data platform with predictive analytics and AI/ML based Smart Features are enabling organizations to benefit from an effective and scalable way to leverage data for strategic business insights. As the technology proliferates, it is helping companies identify business drivers and KPIs. As a result, banks, credit unions and other financial service providers are removing irrelevant data to discover patterns, insights, trends, and usage strategies, helping to strengthen their position in regional markets.
