Seth Rao
The recent collapse of lenders in the US has tested Americans’ faith in regional and community banks that supply credit to a significant portion of the country’s entrepreneurs and businesses. Deposits have flooded into megabanks, leading to a significant decline in smaller banks’ deposits, which could have long-lasting repercussions for the communities served by these banks. Small banks are already struggling with a credit crunch, which could be worsened by deposit swings. Bankers are now stockpiling cash, and the likely result is a credit crunch. Moreover, the speed of the recent deposit runs has led analysts and central bankers to predict a severe credit crunch. (https://www.wsj.com/articles/small-banks-are-losing-to-big-banks-their-customers-are-about-to-feel-it-178abb47)
Trust is a crucial element in any relationship, especially between banks and their customers. People are more likely to trust their banks when they see positive news and hear good stories about them. However, negative news can have a detrimental impact on customers’ trust in banks and financial services firms. Therefore, it’s essential for banks and financial services firms to ensure that their data is reliable and accurate to prevent negative news. Bad data can instantly ruin their good name.
Foundation of Trust: In today’s world, data is king, and it’s the foundation upon which all decisions are made. However, if your data is incorrect or unreliable, it could lead to disastrous consequences for your bank or financial services firm. In the past, there have been several examples of non-flattering news from banks and financial services firms due to bad or incorrect data. For instance, Danske Bank overcharged customers, resulting in negative news coverage and damage to the institution’s reputation. Similarly, Wells Fargo reported incorrect financial data, resulting in regulatory fines, legal action, and negative news coverage, eroding customer trust.
When Trust is lost: After the collapse of the lenders, smaller and mid-sized U.S. banks lost $108 billion in assets in under a month, as per Federal Reserve data. This has made customers jittery about trusting small and mid-sized banks and financial services firms. Making it crucial for them to ensure their customers can trust them and, for them to trust their own data as being reliable and accurate.
Step one for CFO’s of small and mid-sized banks and financial services firms is to establish a strong foundation for decision-making by ensuring the trustability of their data. This will help stop their jittery customers from becoming even more nervous. DataBuck can fix the Data Trustability problem immediately. Powered by Machine Learning, with very little manual effort, banks and financial services firms can establish data trust scores for every piece of data used to make decisions. This will ensure that their data is accurate and reliable, providing a strong foundation for decision-making.
The only solution- Real-time Data Trust Scores
In conclusion, banks and financial services firms need to prioritize the trustability of their data to avoid non-flattering news and protect their good name. Legacy tools like Informatica Data Quality (or Informatica DQ/IDQ), Talent and others have not proven to be reliable solutions. Building a strong foundation for decision-making is crucial in earning and maintaining customers’ trust. DataBuck can help banks and financial services firms establish this foundation quickly and efficiently, ensuring that their data is reliable and accurate. It uses AI and ML technologies to automate more than 70% of the data monitoring process. DataBuck identifies inaccurate, incomplete, duplicate, and inconsistent data, which improves your data quality, trustability and, usability.
Contact FirstEigen today to learn more about solving data quality issues.
Check out these articles on Data Trustability, Observability, and Data Quality.
- 6 Key Data Quality Metrics You Should Be Tracking (https://firsteigen.com/blog/6-key-data-quality-metrics-you-should-be-tracking/)
- How to Scale Your Data Quality Operations with AI and ML (https://firsteigen.com/blog/how-to-scale-your-data-quality-operations-with-ai-and-ml/)
- 12 Things You Can Do to Improve Data Quality (https://firsteigen.com/blog/12-things-you-can-do-to-improve-data-quality/)
- How to Ensure Data Integrity During Cloud Migrations (https://firsteigen.com/blog/how-to-ensure-data-integrity-during-cloud-migrations/)
