As a technologist, it’s encouraging for me to see the growing adoption of AI technology in the financial domain. As we know it, Financial AI is transforming customer experiences, detecting frauds, and even handling customer contracts. AI is also changing the equation in customer-centric services like loan processing and customer onboarding.
Apart from the benefits, there are growing concerns about data privacy and security in the financial sector. In the face of stringent industry regulation, technology leaders in the financial industry are seeking innovative AI-driven techniques to preserve their massive financial data. Privacy-preserving techniques go a long way to facilitate data sharing without compromising on the privacy aspect.
In this blog, let’s understand seven of the latest AI-powered privacy-preserving techniques that financial companies can implement:
- Secure Multi-Party Computation (SMC)
AI-powered SMC is an innovative cryptographic technique, which allows multiple parties to collaboratively compute a function, using private inputs. With this technique, individual entities can compute without providing any sensitive or confidential information.
Fundamentally, SMC breaks down (or divides) any computation into smaller independent tasks. These tasks are then distributed among many parties to perform computation using their local dataset. How does this impact data privacy? As against traditional data mining techniques, this technique does not use centralized data. It allows data owners to collaborate, without the need to share their sensitive information.
- Homomorphic Encryption
Traditional data encryption tools require data access and exposure before the encryption part. This can expose the data to potential privacy risks. AI-powered homomorphic encryption enables data encryption without the need to expose the data.
Additionally, with this AI-powered technique, financial companies can perform computations on encrypted data, without the need for decryption. Besides, the computation results remain encrypted and are later exposed only with a secret decryption key.
Among its use cases, this technique is useful for preserving sensitive financial data like customer records. It is also useful for encrypting AI training models, thus safeguarding intellectual property.
- Federated Learning
Financial crimes like money laundering and illegal transactions continue to plague the financial services industry. Federated learning is an AI and machine learning technique that can efficiently share data to track these unlawful activities. Federated learning also works towards protecting data privacy by detecting data anomalies in "suspicious" transactions.
To detect financial crimes, multiple entities including banks and payment networks must collaborate over diverse datasets. Federated learning allows these entities to train an AI model for anomaly detection.
- Differential Privacy
Introduced in 2006, differential privacy provides a mathematical framework for privacy in data analysis. This AI-driven framework caters to individual privacy within datasets. With differential privacy, financial companies can analyze and share private data without exposing any sensitive information. Besides, this framework is also compliant with industry regulations like GDPR and CCPA.
Among the key innovations, differential privacy provides a "privacy guarantee" against new and sophisticated adversarial methods. As a concept, differential privacy applies to statistical analysis, including those that are not yet developed.
- Secure Enclaves
As financial organizations deploy more AI models, this has also led to a rapid increase in attack surfaces. Traditional security techniques can no longer adequately protect AI models against sophisticated attacks. Secure enclaves offer a more practical approach to improving AI model security and privacy. A secure enclave is a private memory region that protects data through hardware isolation and encryption. This private region is not accessible externally, even when the host machine is fully compromised.
Effectively, this technique eliminates any exposure of sensitive data (like financial records) during data storage, use, and movement.
- Privacy-preserving machine learning
AI-based predictive models depend on real-time data availability to detect patterns and perform accurate analysis. However, data flows also raise concerns about data breaches and financial crimes.
Among other techniques, privacy-preserving machine learning (PPML) facilitates sensitive data collection, while complying with privacy regulations. At its core, PPML provides a step-wise approach to preventing data leakages from machine learning algorithms. This technique preserves data privacy during the following 4 stages:
- Model training
- Feeding input data into the model.
- Providing output data from the model to the client.
- Model privacy
- Data aggregation
Finally, using the data aggregation technique, financial companies can collect and aggregate data from various sources. Data aggregation is also useful for summarizing the data into groups (or cohorts) for statistical calculations. Once aggregated, data is presented in a summarized format like a table, chart, or report.
The AI-powered data aggregation technique simply automates the entire process. AI-powered algorithms can collect and analyze large volumes of financial data in quick time. AI models can also identify data patterns and the latest trends, thus leading to more insightful decisions.
Data aggregation is also a crucial part of MLOps where models can work on the right data entering the data pipeline.
Conclusion
In the face of unrelenting security and privacy challenges, financial companies can no longer depend on traditional techniques like data mining and encryption. In this blog, we have discussed how AI-enabled techniques are enabling data privacy.
With its offerings in financial services, Wissen has specialized in the areas of data management, risk, and regulations. We also provide the best-in-class services in AI and machine learning to address a variety of business problems. As a technology partner, we can help you protect and preserve your sensitive financial data. If you want to know more, contact us now.
