| | NOVEMBER 202319TCS' Open AI in Banking and Financial Services Investments in innovation, particularly in platforms for AI, have gone beyond the realm of discretionary IT spending and are now required. Financial and banking services are also subject to this tendency. Organizations must address the numerous AI principles ­ responsible AI, ethical AI, sustainable AI, human-centered AI, and explainable AI ­ in the context of the banking industry while ensuring the adoption of AI is successful. Developing a plan that takes into account the traits and tenets of AI is the first and most crucial step in the adoption process. As a result, open platforms, open ecosystems, open finance, open data, and open application programming interfaces (API) are becoming typical in the sector. Participants in the banking ecosystem now have more needs than ever because of the open banking phenomenon's wealth of data. Similar to this, open banking is forcing banks and other financial organizations to improve digital contextual services, establish fair competition, and monetize data in order to survive and prosper in a cutthroat market. The realization of value benefits through prospects for direct and indirect monetization, however, depends critically on data analytics.Traditional Data Analytics and Data Visualization ApproachesBy using established and predefined reporting requirements, traditional data analytics and data visualization approaches are ideally suited to produce historical reports and spot data patterns, exceptions, and outliers. The use of these techniques in business processes for real-time decision-making or straight-through processing necessitates specialized knowledge of how to understand data and identify anomalies and potential future scenarios. Here is where the narrative is being altered by technology like AI. These methods can be strengthened by AI, providing chances to learn from past human decisions, uncover hidden patterns, and develop solutions that are prepared for the future and can listen, respond, and react to novel situations.An AI platform created exclusively for internal solutions, however, does not offer enough economic value. On the other hand, the AI platform must adhere to the traits and tenets of AI in order to meet the unique needs of each entity in the ecosystem. Here, managed open intelligence lays the road for AI systems to succeed. Given the compliance and legal requirements in banking and financial services, implementing open intelligence to develop an open platform ecosystem, specification, or standards for AI is more difficult than it is for other open paradigms. The actors in the banking ecosystem are represented in the open intelligence ecosystem by some of the fundamental services they contribute and use.Banks have been preparing for a collaborative competition, and data or information is serving as their link. Open intelligence enables banks to develop a wide range of products and services for customers, authorities, partners in the ecosystem, and business processes. Whereas human intelligence formerly made a difference, banks can now earn a return on investment through business models such as as-a-service, as-a-platform, and more, along with contextually differentiated digital propositions. Open intelligence will aid organizations in generating exponential value from their operations when used in tightly controlled procedures. Additionally, important deciding variables include combining temporal data with business data, investing in analytics platforms as opposed to data science, and creating an internal AI platform as opposed to utilizing commercial solutions. Although these issues can be resolved by altering organizational culture, most of them necessitate the involvement and support of the entire ecosystem in order to produce dependable and stable solutions
< Page 9 | Page 11 >