The Panama Papers marked history’s biggest data leak, the offshore financial activities of prominent individuals and entities based on a leak of millions of documents from Panamanian law firm, Mossack Fonseca. Almost 8 years ago, more than 370 journalists at over a hundred publications around the world simultaneously published the Pulitzer Prize-winning Panama Papers investigation with the International Consortium of Investigative Journalists. The Panama Papers scandal has had far-reaching consequences that will continue to shape the future of global finance and transparency.

A highly visible regulatory and tax authority investigation was prompted by the Panama Papers data leak.

Calimere Point was pivotal in delivering crucial data analytics as part of our client’s regulatory response, leveraging cutting edge cleansing and matching algorithms to help identify potential clients of interest. The leaked documents consisted of millions of files, including emails, financial spreadsheets, and other records.

Outside the high-profile casualties, our clients confronted a seemingly straightforward yet daunting query: “How many of my clients are among the 11.5 million documents?” This question arose due to inquiries from both the IRS and other global & local regulatory bodies.

While identifying a single name in the Panama List proved relatively straightforward, the challenge magnified exponentially when dealing with thousands, or even millions, of names, necessitating quick and efficient solutions.

Navigating the Data Maze

Superficially, matching a company’s client list (called the “Private List”) against the Panama Papers was a straightforward exercise. However, several practical issues immediately became apparent once the data was examined.

Both the Panama Papers and the Private Lists had their own data nuances. Critically, the data nuances were not consistent within a given data set nor between the data sets. The data needed to be normalised to ensure names were matched on a consistent basis.

The challenges in data matching encompass various aspects:

Fuzziness: Employing “Fuzzy Matching” presents challenges such as balancing false positives/negatives and increased runtime.

Iterative Process: Data matching involves an iterative heuristic approach, acknowledging its empirical nature and requiring multiple rounds of refinement for accuracy.

Local Data Privacy Laws: Restrictions on cross-border data transfer pose logistical challenges, particularly in coordinating matching processes across multiple locations.

Data Nuances: Factors like punctuation, company types, suffixes, and word order necessitate meticulous consideration for accurate matching.

The Data Solution

Calimere Point’s response to the Panama Papers scandal addressed the shortcomings of traditional regulatory methods by leveraging cutting-edge technology.

Our rule-based analytics application and data visualisation tools offered a robust solution to the challenges posed by the complex offshore financial system. Endorsed by a Big Four Auditor, our solution ensured regulatory compliance while providing a dynamic understanding of processes. Additionally, our cost-effective approach enabled a coordinated global response to regulators, enhancing efficiency. Furthermore, our client data alignment solution facilitated a comprehensive view of clients across diverse business landscapes post-Panama.

The data cleansing, tagging and matching algorithms developed to address the Panama Papers challenge for our clients have, after several evolutionary iterations, been incorporated into our market leading client alignment solution – SingleView.

Case Study
The Panama Papers

8 years later, the repercussions of the Panama Papers scandal resonate far beyond its initial impact, prompting reflection among governments, institutions, and individuals.

The revelations have sparked a re-evaluation of their respective roles in advancing transparency, fighting corruption, and fortifying international financial regulations.

Read the full case study of the Panama Papers – Matching Account Names with Advanced Data Analytics by Calimere Point.

Full Case Study