by Michele Valerio Alfano, Project Consultant in Excellence Innovation
In the current economic landscape, a company’s data assets represent a strategic asset of fundamental importance. However, its value does not only lie in the collection and organization of data, but is fully manifested when the latter are transformed into decision support tools. CRM systems, which until now have been fundamental for the creation of a structured data asset, can now provide a further contribution. Thanks to the use of new technologies, such as predictive analysis and machine learning, CRMs will be able to identify previous patterns and scenarios. When considered with respect to the banking sector, this evolution takes on even greater particular relevance. In a context in which the management of financial advisors depends on the balance between costs and potential earnings, this new functionality takes on a high value, which allows managers to adopt a “data-driven” approach, helping to generate a “competitive advantage” , strictly linked to the amount of information in one’s possession.
Predictive analysis and possible implications
Predictive analysis systems represent one of the most significant innovations in the field of corporate data management. These tools, based on technologies such as machine learning, go beyond the traditional descriptive analysis approach, offering a vision projected towards the future. Thanks to their ability to process large volumes of historical data, they are able to identify hidden patterns and significant trends. Integrated into CRM systems, these tools allow you to exploit the data already collected and organized, to offer new value opportunities to be used in decision-making processes. The data assets thus become an active resource, capable of fueling forecasts and probabilistic scenarios that reduce uncertainty and improve the quality of strategic decisions.
In the banking sector, for example, predictive analysis can be used to evaluate the potential economic performance of a financial advisor, identifying which is the most realistic economic prospect for that candidate. This ability is not limited to forecasting, but extends to the operational valorisation of information, supporting management with concrete tools. In a context in which the management of uncertainty is crucial, this technology stands as a pillar for companies that want to equip themselves with strategic assets.
The role of the AI-Act
The adoption of predictive analysis in CRM systems opens up enormous opportunities to enhance data assets, however, the use of these technologies requires attention not only from a technical point of view, but also from a regulatory point of view. In fact, at this juncture, the AI-Act, introduced by the European Union, takes over to regulate the use of artificial intelligence. This regulation was designed to prevent risks related to unfair practices or algorithmic discrimination and to promote responsible and controlled use of AI. To implement predictive analysis systems in CRMs, it is therefore essential to ensure compliance with these regulatory provisions. Even more so in the banking sector, the adoption of predictive systems must necessarily respect these constraints, while ensuring that the solutions are designed to offer added value without compromising regulatory compliance.
Impact of the AI-Act on predictive analysis systems
In the AI-Act, a risk-based approach was used to regulate the correct use of Artificial Intelligence systems (which also includes predictive analysis systems), classifying the systems into three main categories:
- Unacceptable Risk: This category includes AI systems that pose a clear threat to people’s security, livelihoods and rights. Examples include the use of manipulative techniques to distort human behavior or real-time biometric surveillance systems without adequate safeguards. Such applications are prohibited by the AI Act.
- High Risk: This category includes AI systems that may adversely affect the safety or fundamental rights of individuals. Applications in sectors such as education, employment, financial services and access to essential services fall into this class. These systems must meet rigorous requirements in terms of risk management, data quality, transparency and human oversight before being released to the market.
- Minimal or limited risk: This category includes AI applications that pose little or no risk to the rights or safety of individuals. For these systems, the AI Act provides minimal obligations, such as transparency requirements, but does not impose significant restrictions.
In the specific case of a CRM used to manage applications from financial advisors, the integration of predictive analysis technologies leads the system to be classified as high risk. This assessment derives from the fact that the system operates in a critical employment area, which influences decisions relating to access to job opportunities. Although artificial intelligence represents only a support tool for decision-making managers, the potential impact on candidates makes more rigorous regulation necessary. This positioning does not block the integration of these systems, but rather requires the adoption of specific measures to guarantee regulatory compliance. The precautionary measures required by the regulation include:
- transparency of algorithms;
- quality of data used for training;
- security in the management of sensitive information.
Adopting these measures not only ensures compliance with regulations, but also helps consolidate the trust users have in the technology implemented.
Verification of predictive analysis systems created before the AI-Act: can it be seen as an opportunity?
Investing in the adaptation of predictive analysis systems is not only a necessity to comply with the AI-Act regulations, but a strategic opportunity to strengthen company competitiveness. It is not just a technical challenge, but a valid opportunity to refine and improve the predictive analysis systems already in use in CRMs. With targeted and well-planned interventions, it is possible to optimize and consolidate the value of the technologies already implemented. Among the priority interventions to be carried out, at the top is the need to conduct an analysis of the quality of the data. This intervention is essential to ensure that datasets are up-to-date, representative and free of bias, thus ensuring regulatory compliance, significantly improving the reliability of forecasts and offering more precise insights for the business. The transparency of algorithms is another crucial aspect: making the decision-making process understandable not only strengthens stakeholder trust, but offers decision-making managers a concrete advantage. Understanding how the system reasoned allows them to evaluate the forecasts with greater awareness, deciding whether to agree or intervene to direct strategic choices in a more targeted way. Furthermore, the integration of monitoring tools allows you to track decisions in real time, facilitating immediate interventions in the event of anomalies. Human supervision in critical decisions ensures safer control, creating an ideal balance between advanced technology and managerial skills.
The interventions described are not limited to satisfying regulatory obligations, but reveal the true potential of the system, transforming it into a strategic asset with concrete and measurable value. With more accurate forecasts, transparency that inspires trust and optimized control, the system becomes an indispensable ally for faster and more targeted decisions.
Conclusion
The adoption of predictive analysis in CRM represents an extraordinary opportunity for companies to enhance their data assets, making it a real strategic engine. In the banking context, in particular, predictive analytics can optimize contact management, offering a powerful tool for evaluating financial advisors and planning investments with greater awareness.
The right key to evaluating predictive analysis is: not just a technological tool, but a key element to drive innovation and growth. Companies that are able to seize these opportunities will be able to transform their approach to data management, obtaining a lasting competitive advantage and differentiating themselves in the banking sector landscape.
