Rapid digitization of the financial sector has led to an exponential increase in PII* data volume. Financial institutions often handle individuals’ account numbers, transaction histories, addresses and employment details, and income information. Effective PII handling is critical for regulatory compliance, fraud prevention, and, most importantly, maintaining customer privacy and trust.
*PII or personally identifiable information is any data that can be used on its own or combined with other pieces of information to identify a specific person, either directly or indirectly. Examples of personally identifiable information include full name, social security number, passport number, credit card information, bank account numbers, etc.
Meanwhile, AI and large language models (LLMs) are being praised as the latest game changers in fintech. Companies are leveraging them to automate various business processes, including data analysis, risk mitigation, real-time fraud detection, and customer support. The continued growth of AI's capabilities enables organizations to serve their clients smarter, faster, and more cost-effectively than ever before without jeopardizing the quality of their services.
However, using third-party AI services, like OpenAI, can prove to have privacy risks when dealing with sensitive financial data. Akvelon applies two efficient approaches to address this challenge: AI-powered data anonymization to mask customers' personally identifiable information (PII) more efficiently before sharing data with external AI service providers or self-hosted LLM-powered data processing solutions that ensure full control over sensitive data.
In this article, we'll explore the benefits of each approach and share our insights into AI PII data management based on our engineers' hands-on experience building compliant AI-powered PII data solutions and other AI-driven applications.
Approach 1: AI-Powered Data Anonymization in Fintech Before Third-Party Processing
One approach is to mask or anonymize sensitive information in datasets before using cloud-based AI services like OpenAI. Data anonymization helps to protect individual privacy while maintaining data utility. And using AI for data anonymization helps automate PII detection and anonymization, and achieve more sophisticated anonymization results than with traditional methods. Other advantages of AI anonymization in fintech include:
Enhanced data protection
You can significantly reduce the risk of re-identification as AI delivers more nuanced and effective anonymization techniques and allows for more thorough removal of PII.Adaptability
AI models can be trained to recognize and anonymize numerous types of PII across various data structures and formats, adapting to your organization's specific needs.Scalability
You can scale your automated AI-powered processes to handle large volumes of data efficiently to fit your company’s growing operations’ volume.Regulatory compliance
This anonymization method aligns with GDPR, CCPA, and other data protection laws, helping your company meet these legal obligations.
We leverage advanced ML algorithms and AI-powered tools to balance strong privacy protection with high data quality. The flow charts below demonstrate PII detection and anonymization on example of Presidio AI by Microsoft – one of the tools that Akvelon’s team applies for data anonymization.
Presidio Analyzer (image from the Presidio documentation)
Presidio Anonymizer (image from the Presidio documentation)
Properly anonymized data retains valuable insights, allowing fintech businesses to leverage the information across various practical scenarios. Below, we’ve gathered several of the most common use cases:
From our experience gained over several years of helping leading fintech companies implement AI-powered data management solutions, we've identified some key challenges that organizations need to consider when utilizing AI for data anonymization in fintech:
Excessive anonymization may make data less insightful, so you need to maintain a careful balance and fine-tune your AI model to preserve essential patterns while removing identifiable information.
Unusual data points or rare combinations of attributes take extra skills to anonymize effectively without compromising utility. It is best to turn to experienced AI engineers, who can adjust your AI model to handle your specific edge cases successfully.
Data nature and structure may change over time, and AI models need to be adjusted to detect new patterns that could compromise anonymity. You’ll need to keep your AI-powered software current to achieve optimal anonymization results.
Solid computational resources are required to implement some of the more advanced AI anonymization techniques. Organizations must also navigate this aspect to ensure that they have stable system performance.
Approach 2: Self-Hosted LLM-Powered Systems for Data Processing
Organizations that work in finance face challenges in AI adoption due to strict data privacy regulations and security requirements. While these don't entirely prevent AI usage, they can complicate implementation of cloud-based AI. Local AI data processing solutions can address these challenges, as they keep sensitive data within the organization's control, thus simplifying compliance efforts and reducing security risks. This approach can be applied to most use cases typically realized in third-party environments, so businesses can still get all the perks of leveraging AI.
Even with local LLMs, there is still a need to go through the data preparation stage. During this stage, we select representative samples and generalize information to train LLMs to understand core concepts and patterns. This approach reduces bias and enables effective AI implementation across diverse data sources.
When properly implemented, local AI data processing shows faster response times for time-sensitive financial operations compared to third-party services, along with several other benefits including:
Reduced data exposure risks
You can minimize exposure to potential data breaches or unauthorized access through in-house management.Faster data processing
Self-hosted AI solutions show higher data processing speed as there’s no need to send data back and forth to a remote server.Enhanced quality in specific cases
Fine-tuning adapts the AI model to your unique data and requests, improving performance on specialized tasks.Enhanced data protection
Self-hosted AI simplifies the compliance process by providing enhanced control over data’s sovereignty.Infrastructure traceability
Your data stays within your organization’s secure infrastructure, allowing end-to-end traceability.
Our experience with deploying local AI solutions for data processing has prepared us to navigate efficiently and to help you resolve unique challenges associated with them. Below are a few things that may be worth your attention:
Implementing and maintaining sophisticated AI infrastructure requires specific engineering expertise with local LLMs, as well as resource-intensive hardware and software.
Self-hosted LLM performance doesn't live up to the level of SaaS solutions out of the box, so you may need LLM fine-tuning that requires specific knowledge.
Raw organizational data can lead to biased or overfitted models. Efficient data generalization helps remove specific details while preserving core patterns, enhancing the model's ability to perform well on novel data.
It is critical to choose the right LLM that will perform optimally in your environment. This selection process will require you to assess several different models that would work for your set of tasks and within your hardware conditions. Below, we’ve gathered some insights into Akvelon’s LLM assessment methodology, which helps us evaluate models for consistency in certain tasks.
Discover details about launching an LLM in your environment, comparisons of different LLM types and capabilities, fine-tuning tips, and more from our article on Empowering Your Business with Local LLMs.
Akvelon’s AI Solutions That Are Transforming Fintech
Building on AI's benefits, we've developed multiple AI and GenAI-powered solutions to optimize different business processes, from solutions for our clients to in-house projects.
For example, we’ve developed a GenAI chatbot that can enhance financial institutions’ customer service. Our chatbot can be adjusted to address client-financial service issues and requests. It also provides immediate, multilingual support, ensuring 24/7 availability and strict regulatory compliance.
We make data extraction from emails a fast and completely automated process with our Multilingual Email Contacts Extraction System. This solution is particularly valuable for financial institutions that handle vast communication data.
Recognizing the critical role of APIs in modern applications, we've also developed an AI-Powered API Testing Tool that dramatically reduces testing time from 600 hours to just 13.6 hours for 50 API endpoints.
Akvelon's expertise extends to language solutions as well. Our LLM-Powered Translation service can be adjusted to deliver domain-specific, high-precision translations, potentially reducing translation expenses by up to 90% per 1,000 characters.
A final example of our GenAI-powered solutions is our DocMate AI, which addresses the often overlooked thought critical area of technical documentation. This LLM-driven solution automates various aspects of document writing and editing, saving technical writing teams an average of 20 hours of work per month.
Leveraging one of these tools for addressing your business challenges can significantly enhance your operational efficiency, reduce costs, and help your business stay ahead of the competition.
Akvelon's Approach to Using AI
At Akvelon, our AI engineering team works diligently to find the most optimal and cost efficient approaches to tackle our clients’ challenges. Here's how Akvelon's AI-driven strategies create sustainable and long-term value for our clients:
Strategic Alignment and Cost Efficiency with AI
We ensure that every AI initiative our team takes aligns with the strategic needs of the business for which it's leveraged. We find and utilize the best approach to solve our clients' real problems, like optimizing spending or boosting productivity. By integrating AI into our service offerings, we ensure that your resources are utilized more effectively, which increases productivity and allows your company to pass on savings to your clients.Data Quality
The effectiveness of your AI solution heavily depends on the data it processes. At Akvelon, we specialize in preparing data for optimal AI-powered results, regardless of its initial structure. We apply cleansing, normalization, and enrichment techniques, ensuring that your AI models can extract meaningful insights and produce reliable outcomes.AI-Turbocharged SDLC
We integrate AI tools into our software engineering, quality assurance, and testing processes to accelerate project timelines while keeping the quality high. Our teams stay more productive and can handle complex tasks with smaller amounts of resources using GitHub Copilot and GitHub Copilot Chat in their SDLC. By applying our own tool, DocMate AI, we streamline project documentation tasks, and those are just a few examples of tools that we benefit from daily.Ethical AI Use
Akvelon prioritizes ethical AI practices, emphasizing transparency, accountability, and fairness in all applications. We actively mitigate algorithmic biases and strictly adhere to privacy regulations to protect stakeholders. To support these commitments, we've developed an LLM Security and Compliance Testing Framework. This algorithm enables companies to assess and enhance the reliability and ethical standing of their LLM-driven applications.
By adhering to these principles, we ensure that our AI solutions are technologically advanced, practical, and aligned with our client's unique needs for sustainable success.
Conclusion
AI has emerged as a transformative technology across industries though is especially helpful for fintech. Its ability to deliver accurate, tangible improvements offers fintech companies and financial institutions a powerful tool to reduce costs and maintain a competitive edge. Early adoption of AI-centric strategies is becoming a critical factor in long-term success.
Thus, adequate PII data protection is crucial, and leveraging AI for this purpose will be more profitable for your business than any other method. Whether deploying self-hosted solutions or customized third-party services, our AI team can help to ensure their smooth integration and management.
Akvelon's team treats our AI initiatives as part of a long-term partnership with our clients, aiming to provide continuous value and support as our clients' businesses grow. Ready to explore how AI can drive your business forward? Let's discuss how Akvelon can help you leverage AI to its fullest.
Ilya Polishchuk
Director of Engineering