Financial services and insurance companies face many challenges — with rising interest rates, inflation, and mounting losses all impacting their bottom line. Compounding these issues is the growing number of Fintech and Insurtech competitors, making it harder for financial services companies to differentiate their offerings and gain the long-term loyalty of customers. 

In this turbulent landscape, many forward-thinking brands are now embracing groundbreaking technologies like Generative AI (Gen AI) to overcome the aforementioned obstacles. Unlike disruptive technologies such as  automation (where the focus lies in optimizing existing processes), Gen AI analyzes both unstructured and structured data to unlock hidden insights that bolster optimization efforts. 

Through Gen AI and the underlying data fabric architecture, financial services companies gain the ability to work directly with data from disparate sources, empowering them to become more agile without relying on technical teams and significant IT infrastructure investment. 

That said, introducing Generative AI in financial services brings its own set of challenges and risks.

This article will highlight how solutions like Stratio Generative AI Data Fabric give organizations the power to streamline admin, reduce operating costs and losses, and deliver hyper-personalized services that boost consumer loyalty. Read on to learn more.

Challenges for brands embracing Gen AI in financial services

Information stuck in outdated infrastructure

Many financial service providers are still dependent on legacy systems, causing their data to be trapped in inaccessible silos. 

According to a recent industry survey by Novidea, 76% of insurers rely on six-to-ten insurance solutions. 40% of these platforms have been in use for five to 15 years, yet only 41% of providers plan to overhaul their digital systems within the next 12 months.

Prioritizing the breaking down of silos is crucial for helping Generative AI tools automate and deliver insights into underwriting, claims processing, and fraud detection. However, replacing infrastructure can still prove to be a highly complex and expensive undertaking. In this whitepaper, we discuss how Stratio can help you in your journey to safely decommissioning SAS Analytics in six months. 

The need to maintain strict compliance protocols

Data governance and risk management are two crucial areas where financial services providers must implement robust provisions. However, industry regulations and risk management protocols constantly change and evolve. 

Deploying Gen AI introduces additional risk into the mix, as businesses must ensure solutions draw from up-to-date customer and company data and adhere to data privacy regulations (such as GDPR, KYC, and HIPPA).

They must also ensure that customer-facing AI remains a trusted source of accurate information. Consequently, there’s a strong requirement for AI models to promote transparency and explainability. 

The soon-to-be-launched EU AI Act adds another layer of complexity. For example, if a financial services company wishes to use AI models to determine access to financial products, the providers must make sure that the AI models are fair, unbiased, and accurate. Therefore, financial institutions will need to adapt AI practices to comply with the Act’s requirements for risk management and transparency.

Not many AI providers can, at present, guarantee the complete traceability of AI models. Stratio is one of the few companies that can support the Act’s transparency requirements for general-purpose AI models. Our products are  designed to give organizations a better understanding and additional risk management capabilities, resulting in highly impactful AI models. 

For instance, it can meet transparency obligations such as self-assessment and mitigating systemic risks. It can also handle serious incident reporting, conduct test and model evaluations, and adhere to cybersecurity regulations. 

Stratio makes this possible by keeping your data secure in its original location. From there, it translates technical data into clear industry-specific terms using ontologies. Granular security and data governance controls ensure that only users with the right permissions can access sensitive data. Then, its Gen AI engine, powered by multiple Large Language Models (LLMs), verifies the accuracy of outputs using enterprise data. It only sends relevant information to external knowledge bases, ensuring you only receive insights based on the most accurate, up-to-date information. 

Balancing personalization with responsible AI deployment

While Gen AI has the power to understand customers’ needs, and provide them with the personalized experience the majority of consumers crave, there are still widespread concerns about the ethical deployment of Gen AI tools. 

For instance, research from Ipsos found that only 43% of consumers trust AI not to deliver biased outputs that lead to discrimination. Findings from Accenture also reveal that 49% of insurance customers trust human advisors more than chatbots when making a claim. 

In contrast, IBM found that 38% of financial service customers value self-service tools, suggesting that brands need practical solutions for blending human ingenuity with advanced AI to help them sensitively yet efficiently deliver services. 

Going back to the EU AI Act, the regulations mandate that all interactions with AI tools be clearly labeled to empower customers to decide whether to interact with a human representative or are happy to proceed with an AI tool. Additionally, the Act requires complete transparency in AI-powered decision-making. Therefore, financial institutions need solutions that can meet these transparency requirements.

Stratio Generative AI Data Fabric delivers a wealth of benefits for financial services brands

Stratio offers an end-to-end enterprise data-centric solution for insurance and financial services brands looking to harness the benefits of Gen AI. 

Our Generative AI Data Fabric unifies, governs, and democratizes data access — delivering real-time analytic capabilities to spur business decision-making. Its low-code, modular architecture ensures financial services companies can explore automation and develop new applications and analytic models quickly to keep up with market demand. 

Let’s take a closer look at some of its use cases, and how the solution delivers rapid ROI: 

Breaking down data silos to enhance loss-prevention analysis

Stratio’s unified business layer can enhance profitability by analyzing claims data from multiple sources and identifying areas prone to losses. These insights can then be used to bolster risk mitigation strategies. 

For example, an insurance company approached Stratio to unite data sources in a wide range of formats and build a trusted data model for fraud prevention analysis. 

Within the first three months of the model’s deployment, the company reduced fraud losses by 20% — all thanks to the platform’s ability to compile vital insights effectively. 

Prioritizing customer needs and privacy with governed data

Stratio applies data governance and enterprise security protocols at every stage of the data lifecycle, empowering financial services brands to analyze and develop personalized product recommendations.

An insurance company used Stratio to create virtualized copies of customer data from multiple sources and develop an AI-driven propensity model for sales and personalization. With its predictive analytics capabilities, the model could accurately define commercial actions across numerous channels while self-analyzing its impact. 

What’s more, the unified business layer applied data privacy and source traceability rules, ensuring compliance. Within the first month of deployment, the model generated an additional $10 million in revenue for the company. 

Real-time agility in analyzing disparate sources

Stratio promotes interoperability, enabling users from all technical backgrounds to gain real-time insights into consumer behaviors from various information sources. 

To illustrate, an auto insurance brand wanted to become a market leader. It sought out Stratio to help the team unify data from website activity and third-party providers to facilitate advanced market segmentation and real-time pricing recommendations. 

Stratio enabled the insurer to create in-depth customer profiles capable of delivering automated marketing messages to boost sales. Its low-code features also helped the brand customize outputs and improve the service over time, resulting in a 20% sales boost.

Stratio streamlines Gen AI in financial services deployment

In 2023, we were recognized in Gartner’s Magic Quadrant for Data Integration Tools, highlighting our focus on delivering future-proof solutions aligned with our client’s unique business goals. 

We take financial services companies through our five-step framework for Gen AI integration success, incorporating the following steps: 

  1. Data Auto Discovery: We automate the discovery of all enterprise data in line with your data governance and security rules. 
  1. Data Virtualization: We safeguard data integrity by creating virtualized copies of data in situe, facilitating real-time analytic capabilities. 
  1. Data Marketplace: We give your data meaning through enterprise and industry-specific ontologies, enhancing data democratization across your user base. 
  1. Enabling AI: We deploy Machine Learning (ML) algorithms to enhance the semantic ontologies and build knowledge graphs of your data, providing more contextual information. 
  1. Instant Answers with Gen AI: A final Large Language Model (LLM) layer is applied; capable of accepting natural language queries and delivering fast and accurate answers based on user roles and access permissions. 

To learn more about Stratio’s advantages in modernizing financial services, contact us for a free demo. 


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