Empowering financial services through decision intelligence

This article was written by Jak Hart, Sal Amindavar & Seth Roy and was originally published on the Capco website. 

The financial services industry faces immense pressure to generate new revenue streams, optimize costs, and enhance risk management. To address these challenges, Decision Intelligence (DI) is emerging as a powerful solution, helping enterprises achieve their objectives in an effective and streamlined fashion.

By combining various disciplines and leveraging AI-powered automation, DI empowers enterprises to make data-driven decisions more effectively and efficiently. Through DI, financial services organizations can extract valuable data insights faster and improve decision-making at scale, gaining a valuable competitive edge in today’s dynamic markets. Broad access to DI technologies further benefits the wider enterprise significantly.

Below we explore how financial services organizations can best leverage DI for greater operational and commercial impact.

Unlocking opportunities with data-driven decisions

Through the application of advanced analytics and machine learning, DI can enable a comprehensive understanding of customers, clients, operations, and market dynamics. This data-driven approach facilitates real-time decision-making across all business operations, unlocking opportunities for innovation, cost savings, new value adds, and brand enhancement.

With DI, financial institutions can better create tailored products and services that cater to specific customers’ needs and foster new revenue streams through more granular insights. Additionally, DI enhances the visibility of cross-selling and upselling opportunities, presenting further avenues for innovation and growth.

DI solutions can aid financial institutions in automating and streamlining business processes through embedded analytics, machine learning, and AI tools. These tools can analyze and facilitate improvements to streamline process loan applications, manage basic customer service inquiries, and reduce the reliance on human intervention for these routine tasks. By streamlining processes and reducing the headcount required for manual tasks, DI frees up employees to focus on strategic and value-added opportunities. By 2030, financial institutions are projected to achieve a 22% cost reduction across their front, middle, and back offices.

Through the analysis of large volumes of customer data via machine learning techniques, businesses can also gain a nuanced understanding of customer preferences, behaviors, and motivations. Equipped with these insights, they can offer highly personalized recommendations, create customized investment portfolios far more closely aligned with each client’s financial objectives, and deliver targeted marketing campaigns tailored to their interests and lifestyle choices.

Additionally, the ability to identify profitable customer segments and uncover new business and partnership opportunities bolsters opportunities for revenue generation. By analyzing internal and external data, financial institutions can determine which customer groups offer the highest potential value, thereby optimizing their acquisition and retention strategies. They can also examine data trends to identify complementary products and services, as well as partnerships with other organizations that offer mutual benefits.

Leveraging these advantages, financial institutions can gain a competitive edge by providing superior customer experiences, forging more strategic alliances, and capitalizing on new opportunities faster than their competitors.

Effective risk management and decision intelligence

DI goes beyond unlocking new opportunities – it also greatly improves risk management capabilities by instantly detecting signs of fraud, money laundering, insider threats, and other dangers across countless transactions. AI models can also evaluate the effects of various economic and financial policies on an organization’s risk exposure, allowing for swift responses to emerging threats. DI improves risk management in specific ways:

• Machine learning and AI allow for the collection and analysis of vast amounts of data in real time, which can be used to identify potential risks and anomalies that may otherwise have gone unnoticed. Using predictive modeling, advanced analytics can identify patterns and trends in data. Machine learning algorithms can then be applied to this data to automatically detect and alert on any deviations from these patterns. Additionally, big data analytics can be used to integrate multiple sources of data including structured and unstructured data, to provide a more comprehensive view of potential risks.
• AI models can simulate a range of scenarios, determining how various events may affect risk levels and facilitate contingency planning. This allows organizations to anticipate potential threats and prepare mitigation strategies well in advance. For example, an AI model can be used to simulate a potential cyber-attack on a financial institution. The model can then be used on historical data to identify potential attack vectors and simulate the impact of an attack on the institution’s systems and data. The information can then be used to develop various plans, which can be activated in the event of a real attack.
• AI enables a more comprehensive view of enterprise-wide risks. By analyzing and integrating data from multiple sources, DI can reveal hidden connections and interdependencies between risks that may arise from external factors such as changes in regulations and market conditions across all business units, products, and functions.

Empowering financial institutions with decision intelligence

Decision Intelligence can only succeed when the right talent, technology, and governance are brought together to enable an enterprise to scale and accelerate the use of data.

The combination of Capco’s data and analytics capabilities and Pyramid Analytics’ business intelligence platform empowers financial institutions to make informed decisions, mitigate risks, and improve operations. This partnership enables clients to analyze vast data, identify patterns, and derive actionable insights, unlocking unprecedented value and addressing pressing challenges.

References

¹Autonomous NEXT Report on Augmented Finance and Machine Intelligence Shows How AI is Disrupting the Financial Services Industry (cardrates.com)

²2023 Gartner Magic Quadrant for ABI – Pyramid Analytics