Continuous intelligence is at the heart of fast-paced digital business and process optimization, decision automation, AI and real-time analytics
In the face of digital transformation, the banking sector has evolved to become a customer-centric and data capable organization. Banks generate large volumes of internal (customer accounts, payments) and external data (macroeconomic variables, customer preferences, social media) across various channels. This is further compounded by the increased velocity and variety of data generated. Banks have to now generate business insights at very fast pace from complex data, of any data source (current and historical), of any data size and format.
Enter Continuous Intelligence!
CI is a new age artificial intelligence (AI)-based advanced analytics to meet the dynamic data demands of the banking sector. Gartner defines as a “style of work in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to business moments and other events. It provides decision automation or decision support. CI leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management and machine learning.”
CI, also known as real-time analytics, exists in a frictionless state and enables a business to leverage continuous, high-frequency, intuitive insights from data sources. The machine-driven system gives organizations the ability to intelligently merge disparate data sources; providing a complete data story and adaptable action points. According to Gartner, CI is “at the heart of fast-paced digital business and process optimization, leveraging decision automation, AI, real-time analytics, and streaming event data.” It estimates, by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.
Speed is everything!
In recent years, AI has significantly impacted the banking sector. For organizations, it has become increasingly important to innovate and stay ahead of their competition. CI requires nothing but the real-time availability of data – a challenge for the banking sector that is built on enterprise information hubs, data lakes, and other disparate systems. Banks can get started by bringing in the correct data integration and real-time replication capabilities. AI solutions help speed up data collection across silos and automates the generation of data stories. For example, banks can leverage analytics to determine risk-score of customers seeking loans. With CI, banks can now simultaneously leverage insights across both information asset repository and real-time transactional data.
By combining real-time data with legacy data, banks are in a position to take banking customer analytics to a new level. Contextual insights would help drive a multi-dimensional decision-making process. For instance, Korea’s NH Nonghyup Bank is transforming its business operations with machine learning from SAS. NH Bank is adopting machine learning and AI to better understand its customers and provide a personalized customer experience. To meet the speed, agility and growing needs of the company, NH Bank looked to advanced analytics dashboard that provides a complete view of each customer. The portal is said to support both structured and unstructured data allowing business users to run and generate a variety of visual reports.
Acquire, Identify & Respond quickly
Complex Event Processing (CEP) is an example of application of CI that uses information contained in business events (streams) to identifying event patterns (extract), analyze and provide insights into the changing condition. In retail banking, CEP can be deployed to aide challenges of fraud detection and cross-selling. For instance, the system can detect a series of unauthorized micro-transactions across multiple locations or cards in real-time. Suspicious transactions can be flagged immediately; reducing losses to the customer and ensuring timely corrective actions.
The contextual nature of CI helps organizations to provide a better personalized and interactive experience to consumers. Consider retail shopping – a consumer using an ATM at a shopping center is offered discounts or deals based on their location and the stores nearby, in real-time. The offer provides a better contextual relevance to the customer than the conventional e-mail sent a few days later. The real-time contextual insight can help develop better omnichannel marketing strategies.
Continuous Intelligence turns data into outcomes
Gartner has Identified Continuous Intelligence as a Top 10 Data and Analytics Technology Trend for 2019. Today’s modern applications are driving the development of digital services that are reliant on CI. Across industries, machine learning is easing the journey toward autonomous data. Undoubtedly, AI-based data analytics is of utmost importance to financial institutions and their analytical models. The major shift in data analytics is the accessibility of insights and intelligence to every business user and internal stakeholders. By developing tools around CI, banks can better manage the flow of business models, right from ideation to production.
CI is achievable! Business leaders must take the initiative to leverage their data through new technologies.
