Banking

Predict fraud attempts, reduce lending risk, and stay compliant with data privacy regulation
Banking

Digital transformation.

Banks are facing cybercrime and money laundering. Legislators therefore impose increasingly strict regulations on financial institutions to counter this. Customers expect their bank to handle their data with discretion. Both with their personal and financial data. At the same time, customers want (digital) services from their bank that are tailored to their personal needs. That means: products and services tailored to their personal needs. Financial institutions thus operate in a complex field of tension.

Sufficient reliable data is needed to properly detect fraud and calculate risks to provide credit. Without data, banks cannot make strategic and operational decisions. However, as a result, they lose a significant competitive advantage. Banks and other financial institutions must therefore use this data without breaking the law or violating their customers’ privacy.

Challenges.

Third-party and publicly available data complement the existing sources that banks can draw from. However, fears of privacy and of not being compliant make the use of data a risk.

Another complicating factor is that banks are divided into silos. Business lines act in isolation: data owners and data consumers are separate entities. Existing data sets are often too small or incomplete to accurately train predictive, machine learning models.

... Without data, banks cannot make strategic and operational decisions

... Fears of privacy and of not being compliant make the use of data a risk

Use cases

Fraud detection

Robust and accurate fraud models

Cybercrime and other forms of fraud pose a threat to banks. Anti Money Laundering (AML) models reveal patterns of criminal activity. With sufficiently accurate data, banks can improve their fraud detection models. Using the data, they can simulate risk scenarios and refine risk management strategies. With synthetic data, banks can still create high-quality models to counter fraud.

Credit scores

Making accurate decisions in granting loans

Lenders want to be able to make careful decisions on providing credit. They want to understand their customers' creditworthiness as well as possible. With synthetic data, financial institutions can calculate customer credit scores using a more robust approach. This allows them to make informed decisions and reduce risk.

Stress testing and scenario analysis

Identify and address weaknesses in a timely manner

Banking supervision conducts stress tests to identify how well banks are resilient to financial and economic shocks. This helps supervisors identify weaknesses at an early stage. And address them in consultation with the bank. Real-world data to design diverse scenarios is difficult or even impossible to obtain. Synthetic data offers a solution here.

Software development and testing

Securely share sensitive data with third parties

Digital products with personalized services require data exchange with third parties. Examples include mobile banking apps and customer relationship management software. Synthetic data enables banks and financial institutions to share data for software development and testing securely.