The Basics of Financial Data Collaborations

The data that financial institutions collect from their clients and partners is extremely valuable. However, it only offers a limited perspective. Suppose banking and financial companies can collaborate with other organizations using their collected data. In that case, the resulting data collaboration can lead to powerful new insights and a wide range of resulting business benefits.

But what is data collaboration exactly? A good data collaboration definition is pooling insights from data across various sources to unlock valuable insights for all participants. However, how individual organizations define data collaboration has some variance, including whether the collaborations are happening internally or externally. In the financial services industry, these insights could lead to the development of innovative products, better customer service, and privacy-preserving analytics that may increase information-sharing effectiveness within national and international financial fraud prevention and regulatory compliance.

It is essential to point out that this type of collaboration requires overcoming many common data problems. There are problems with data access, data transformation, and data bias. Value-generating insights are increasingly uncovered through artificial intelligence, and this technology requires massive amounts of information from various data sources to be effective.

Simple Principles for Data Collaboration

A fundamental principle for extracting the most value out of data collaboration is the development of user-friendly workflows adopting data protection and governance.

Rather than having decision-makers sift through dozens of spreadsheets and workbooks to identify insights, top financial companies often rely on business intelligence dashboards and reports that display easily digestible information. These dashboards focus on tracking key metrics in the same way retail stock trading platforms provide information on an investor’s portfolio.

While these dashboards may appear simple, the technology behind them is not. Artificial intelligence processes large amounts of complex data to derive insights meaningfully. Most business intelligence platforms now incorporate AI features that help business analysts quickly make more informed decisions. This can potentially allow operational benefits like portfolio position reporting or resolving customer service tickets quickly.

Data used to drive business insights should be easy to understand and query. We may romanticize the idea of game-changing insights surfacing from extensive data analysis. Still, data-driven insights may confirm leaders’ suspicions based on professional experience and data quality. And, when an analytics platform does produce a surprising result, it’s essential to know the data sources from where the insights came from and have the ability to audit the data.

Data aggregation and validation are simpler when companies work with good data sources. Companies should also create a culture that supports a collaborative, constructive, and timely dialogue preventing valuable insights from being discarded and delaying critical decisions.

Benefits of Data Collaboration

Increased access to financial services
Better customer experience
Better financial products
Greater efficiency
Greater fraud protection
Efficient workforce distribution

Data Collaboration and Increased Access To Financial Services

According to research from McKinsey, data collaboration can lead to economic value in several different ways, including increased access to financial services. When banking institutions understand what services customers need access to, when they need them, and how they prefer those services to work, they can deliver exactly what people need, when and how they need it.

Data Collaboration and Better Customer Experience

Likewise, data collaborations can yield a better customer experience, the same McKinsey research found. For example, identifying data patterns related to how long people wait to reach a representative on the phone, how long certain calls tend to last, and how often and at what point people tend to get tired of waiting on hold before hanging up can all inform staffing strategy to ensure that customers feel like they can reach a live person easily.

Data Collaboration and Better Financial Products

According to a survey from Gartner, company leaders that promote data sharing and the dissolution of data silos often trigger a higher value return from their analytics teams. The research company also predicted that companies that share data will comprehensively outperform rivals that do not by 2023. Part of the reason for that may be that armed with data, banking institutions can engage in better decision-making about what kinds of financial products may do best with their existing and potential customers.

Data Collaboration and Greater Efficiency

Data collaboration can mean a more agile rollout of new products and services through greater efficiency. For example, when an investment bank partnered with tech company Altimetrik, they were able to leverage internal data to improve their application development teams’ productivity by lowering the back-end requirements of new applications. That means that the banking organization can respond more quickly to changing customer demands with new applications and online services that can be developed and launched swiftly.

Data Collaboration and Greater Fraud Protection

Data collaborations can also address threats related to fraud and other criminal activity. When banking organizations have a more comprehensive picture of their clients’ internal and partner financial institutions’ financial transactions, it helps them identify suspicious activity within an expanded ecosystem. Data collaborations can also improve credit risk modeling, ESG portfolio construction and detection of financial fraud based on alternate data sources. Insights from data collaborations and privacy protection scenarios help financial services companies to protect data in use and transit. It supports the processing of data with confidentiality in artificial intelligence and business intelligence applications within untrusted computing environments like the public cloud.

Data Collaboration and Efficient Workforce Distribution

Earlier, we used the example of how data collaboration can help banking institutions determine appropriate staffing levels for inbound calls so that customers are not stuck with long wait times to speak with a live person. However, data collaboration can help with all facets of efficient workplace distribution. That may be particularly important when it comes to companies with many branches or offices, where shifts in staffing may make sense depending on the times of the year or other external factors.

The Challenges of Data Collaboration

Data collaboration needs a lot of data to provide useful insights. Unfortunately, there are many security and privacy challenges associated with data collaboration efforts. Companies do not want their financial information to be shared without discretion. Even if they did, financial institutions have strong business motives for keeping critical information to themselves and protecting their intellectual property while complying with privacy regulations.

Risks are involved when a company enters into a data collaboration with another organization. The data could be intercepted or misused by other participants during the collaboration process. This could be detrimental to both the organization and its customers. In October 2020, hackers breached a Facebook data partner to run targeted ads based on Facebook data for a money-making scam.

Additionally, the sharing of financial information could violate privacy regulations. In the United States, the Gramm-Leach-Bliley Act (GLBA), and CCPA outline several privacy guidelines for sharing an individual’s financial information.

Regulations related to the sharing of personally identifying information, such as date of birth and social security number. In Europe, the GDPR act strictly outlines how organizations may use personal data.

Finally, there are gray areas around the use of data on an individual — actions that are not necessarily illegal or immoral but potentially bear a reputation risk for business.

We Provide More Significant Data Privacy and Control for Data Collaboration

Financial institutions are looking to unlock insights to improve customer experience, increase market share, reduce risk, and drive innovative offerings through data collaboration using privacy-enhancing technologies. While approaches like masking, tokenization, differential privacy, and synthetic data can be helpful, the TripleBlind solution compares favorably with these and other privacy-enhancing technologies. Our innovations radically improve the practical use of privacy preserving technology by adding true scalability and faster processing with support for a majority of data formats and machine learning algorithms that can be deployed on cloud and on-premise platforms.

Our patented one-way encryption technology approach ensures that data and algorithms can never be decrypted and only permits authorized operations. Best of all, the TripleBlind Solution is available through a simple API, and we never take possession of any data, algorithms, or answers.

Book a demo today if you want to learn more about how our solution enables data collaboration.