Financial institutions have focused primarily on the speed and efficiency of transaction processes to minimize the cost and enhance the visibility of information. Depending upon this data, financial institutions provide relevant and appropriate information to their customers that support their revenue streams like an asset, portfolio, risk and regulatory management. Moreover, by streamlining and automating their processes and eliminating paper-based processes, the organizations have improved their time of disseminating the information and providing critical insights to their customers.
The best-in-class companies have streamlined and automated their processes. They have extracted intelligence from the available printed documents using cognitive document recognition and digitized them so that they can be used efficiently. As these companies use robotic process automation (RBA), they are a big-time competition for other organizations which still lag significantly behind in its adoption.
These leading financial firms depend upon technology to mine relevant data from a plethora of complex and diverse source to enrich and analyze it across many topics. These firms are continually working to improve their competitive advantage over the leaders to stay in the competition. Best-in-class companies are leveraging the new technology to provide better intelligence as they are moving beyond the processing advantages. They have adopted machine learning to expedite the recognition of patterns and recommend suitable alternatives that create enhanced insight as AI can more quickly find models which were not seen before. Increased RPA can mine these patterns faster thereby keeping the organization ahead in the pack.
Here are a few advanced capabilities to enhance revenue streams for these financial institutions to stay ahead in the competition.
1. Asset Management: Major reduction is witnessed in unproductive time spent digging through data sources, both internally and externally.
2. Regulatory Compliance: Classification process speeds up with reduced costs and exhaustive monitoring.
3. Retail Banking: Enhancing customer service experiences by minimizing the effort needed to access relevant content in various enterprise applications.
4. Risk Factor: Use of machine learning and AI to detect patterns in the unstructured data to further minimize the risk and optimize performance.
5. Investments: Using dashboards to create a push-based solution by presenting the relevant information in real-time.
Top Notch companies are striving hard to advance their customer insight capabilities and remain ahead of their competition as their business depends upon the value they provide their customers through visibility and insights.