That is why all FinServ orgs including banks spend huge sums of money on avoiding the risks of compliance violations, and because of the sheer scale of the institutions, and the complexity of the data and regulatory landscape, this problem is best solved using a data lineage graph database combination. The potential costs of failing to comply are often measured in the millions and tens of millions of dollars and more per incident in an organization.Īs a way to describe the magnitude of the consequences, most of the top US banks have paid tens of billions of dollars in penalties over the last two decades, with the top offender paying over $80 billion. SOX, GDPR etc), FinServ companies in particular face even more regulation whether it be through regulatory and enforcement organizations such as the SEC, FINRA, FinCEN, OCC, CFPB, FTC, CFTC (and many more), and/or the many laws passed to protect the consumers and even the economy itself against risk (e.g. Challenges of FinServ Regulatory Complianceīeyond even just the many regulatory laws and regulations facing all organizations across all industries (e.g. In the financial services (FinServ) space, due to the significant and increasing regulatory compliance required (in response to many high impact financial crises over time), the reporting/audit demands, as well as the level of difficulty of those demands, it now represents hundreds of billions or more across the world in sheer cost in penalties and effort and a corresponding level of technical complexity and difficulty that is unprecedented. Graph Data Lineage for Financial ServicesĪs discussed in more detail in our article What is Data Lineage and Data Provenance, data lineage (and the related concept data provenance) is critical for managing regulatory compliance and audits as the most financially impactful context, but also reliable application development and analytics decision-making are among other contexts it can be critically important. While graph data lineage capabilities (the data lineage graph database combination) are ultimately suited for every industry, given the unique regulatory burden in the financial services context, it becomes even more critical technology.
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