Financial Intelligence as a Platform Business

We have all read about network effects and how they underlie the business models of fast-growing and high-margin companies like Facebook, Google, Netflix, and Apple. The book Platform Revolution by Parker, Van Alstyne and Choudary seeks to distill the key characteristics of these so-called Platform Businesses, whose overarching purpose is “to consummate matches among users and facilitate the exchange of goods, services, or social currency, thereby enabling value creation for all participants”. Importantly, these Platform Businesses can achieve much higher growth and higher profit margin compared to traditional companies because

  1. they have the ability to create value using resources they don’t own or control, and
  2. they create value by serving the communities on the platform in such a way that increases in the number of consumers and producers of services benefit the others in a non-linear way (the network effect is such that the connectivity between entities increases at a faster rate compared to increases in the number of entities).

When looking at the business models of companies like Uber shown in the Platform Revolution book, it struck me that Financial Intelligence Units (FIUs) like AUSTRAC can be understood as a kind of Platform Business too. FIUs certainly don’t own or control intelligence-gathering resources (which sit in so-called reporting entities like banks, financial institutions, casinos, etc) and law-enforcement resources (which sit in partner agencies like Federal Police, Tax Office, etc) but they create value by matching intelligence from reporting entities with law-enforcement investigations and generating new cases by sharing law-enforcement priorities and typologies with reporting entities. The following diagram is my attempt to capture the essence of a Financial Intelligence Unit as a Platform Business.

Virtuous cycle of Financial Intelligence value creation

Such a so-called two-sided market with both intelligence producers (the reporting entities) and intelligence consumers (law enforcement) can give rise to four kinds of network effects: same-side effects, both positive and negative, and cross-side effects, both positive and negative. The diagram above mainly shows the cross-side positive effects, where law-enforcement agencies and reporting entities can benefit each other by acting in a collaborative manner. The key to encouraging such collaboration is to build mechanisms to allow feedbacks to flow both ways, and this is one of the most important jobs of a Financial Intelligence Unit.

What is not shown in the diagram are

  1. the cross-side negative effects, which can arise
    • when there is a mismatch between the capacity of law-enforcement entities and reporting entities on the platform, which can lead to bottlenecks in intelligence gathering and law-enforcement actions, or
    • when the data volume produced by the reporting entities is so large and the value of intelligence so incomplete as to make the FIU curation — matching of intelligence with cases and generation of new cases — ineffective.
  2. the same-side negative effects, which can arise
    • when multiple law-enforcement agencies cannot perform effective deconfliction when approaching reporting entities and working on cases, or
    • when multiple reporting entities all produce only piece-meal intelligence products because they cannot effectively exchange information with each other (because of a tipping-off provision under most existing anti-money laundering legislations).

Designing the legislation, compliance, and intelligence curation regime in such a way to minimise these negative network effects is another important job for Financial Intelligence Units.




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