AI adoption and utilization in data sensitive industries – financial services

During past 2 years, Sulava has driven AI adoption in several industeres where data sensitivity is critical such as financial services, healthcare and infrastructure providers. In this article we take a look at examples how things are handled and what kind of benefits our customers have found specifically in financial services industry. The examples are easily utilizable for other industries.

Sulava has done Copilot and AI adoption for 20+ financial services customers such as banks, insurance companies and pension funds during the past year. Between these organizations, we have a potential of 50,000+ employees taking advantage of AI productivity leap.

In past years many of these firms selected Sulava as their partner in digital transformation, development of their IT infrastructure and security.

Meeting financial customer security standards and regulations

For financial sector clients, it is mandatory to implement comprehensive data protection, privacy and cybersecurity measures when adopting AI, to comply with financial regulations such as DORA. These measures are a prerequisite for financial organizations to be able to utilize AI solutions and deploy products. Therefore, it is crucial for the sector to establish proper readiness using Microsoft Purview, SharePoint Advanced Management and data classification. This enables Copilot to be securely used with confidential organizational data.

Working with our customers, we have been able to find a model that enables the adoption of entirely new tools while fully complying with regulations. These same models can be applied in other customer sectors with sensitive information.

To meet the regulations, we are implementing advanced capabilities such as Microsoft Purview Suite for internal risk management (insider risk teams are mandatory to financial sector customers by the regulations), Data Loss Prevention etc. Earlier this year Microsoft added the capabilities of SharePoint Advanced Management in Copilot license. The features included allow organizations to effectively identify oversharing in their existing SharePoint Online data.

When adopting AI, data classification and the deployment of data protection and security tools, are tailored to the customer’s starting point. For example, Microsoft E5/Purview data security deployment can be carried out both before and alongside the broader Copilot rollout.

To safely leverage AI, the financial sector must adopt secure tools—such as the built-in capabilities of Microsoft Copilot offer. Without enabling the use of secure tools, employees may turn to consumer-level services, which do not meet the security and compliance standards. With Microsoft Copilot, organizations can ensure that all data remains within protected environments.

Efficiency, productivity, improved service quality – examples of customer benefits

With Copilots, employees in financial sector organizations can leverage AI in their daily tasks, improving productivity, efficiency and well-being. AI supports, for example, the summarization of large document packages and the identification of key points—common tasks in the financial industry.

Information discoverability is one of the most accessible productivity gains AI offers. For instance, in banking customer service, AI enables a significant leap forward by quickly and accurately retrieving and cross-referencing information from multiple sources for example from the internal instructions on the company intranet —ultimately improving service quality. In the constantly evolving financial sector, searching for and applying information is a time-consuming process for employees, so the benefits of AI become evident quickly.

Banks are also facing an exponential increase in regulation and reporting requirements, which traditionally demand more personnel. The productivity gains from AI are especially valuable here, as the same number of employees can accomplish more—for example, in report generation, internal regulatory communications, and material analysis and summarization. AI can easily find for example information about EU regulations and compare the requirements to a specific report.

Our finance industry clients have also shown particular interest in using AI to work with table-based information and large datasets. Copilot in Excel and new Python-supported analysis capabilities have been of special interest to them, and Sulava has provided special training for those topics. Microsoft continues to increase the number of available tools and their capabilities for the analysis of figures within existing solutions and by bringing new alternatives, such as Analyst agent in Copilot Chat and GPT-5 large language model. Significant improvements in this area arrived during fall.

AI enables organizations to increase the productivity of their personnel and processes and offer a better experience for their employees and customers. Here are some of the longer term examples of how financial services organizations can reach organic growth via customer experience with the help of Copilot, AI agents and solution development:

• Interactive client portals: Developing interactive, AI-powered client portals with real-time portfolio updates, market news, and personalized insights.
  
• Streamlined loan processing: Automating and accelerating the loan approval process by analyzing applicant data with AI, reducing processing time and improving customer experience.
  
• Personalized banking services: Using AI to provide customers with tailored banking services, including personalized loan and credit card offers based on their financial behavior.
  

Some examples for the improved profitable growth, operational efficiency and enhance competitiveness can be:

• Real-time financial analysis: Utilizing AI for on-the-fly financial analysis, providing bankers and traders with instant insights into market conditions, portfolio performances, and risk assessments.
  
• Credit risk modelling: Using AI to develop sophisticated credit risk models, predicting loan defaults, and managing credit portfolios more effectively.
  
• Algorithmic trading strategies: Assisting in the development and refinement of algorithmic trading strategies by leveraging AI’s advanced analytics capabilities.
  
  

Some other useful use cases that are important for financial sector customers:

• Employing Copilot to monitor and ensure compliance with global financial regulations, using AI to navigate complex regulatory landscapes.
• Implementing advanced algorithms via Copilot to detect and prevent fraudulent activities in real time.
• Analyzing social media and news trends with Copilot to gauge market sentiment.

Naturally, this all has to be done in line with the European Union AI Act.

Writers: Katriina Saransaari, Stanislav Chakhovich, Arttu Arstila

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