Story.
1. Data Sourcing
Public data sources related to money laundering present a wide range of challenges. The data sources are fragmented, credibility and relevance are uncertain, and the universe of discovery is often unknown or difficult to ascertain.
2. Data Collection
Inherent challenges also exist with data collection. Data can be retrieved by pushing (email/alerts) or pulling (research). Pushing data inundates you inbox clouding important communications. Pulling is time consuming and inefficient with multiple clicks, memory-based navigation or instruction.
3. Data Processing
Data processing poses a unique set of challenges as well. Extracting meaningful information is tedious. Exploiting the data to facilitate critical decision-making can involve more email, links or attachments. Storing the data encompasses problems with infrastructure, cost, scale and accessibility.
Problems we are solving.
1. Data Sourcing
Public data sources related to money laundering present a wide range of challenges. The data sources are fragmented, credibility and relevance are uncertain, and the universe of discovery is often unknown or difficult to ascertain.
2. Data Collection
Inherent challenges also exist with data collection. Data can be retrieved by pushing (email/alerts) or pulling (research). Pushing data inundates you inbox clouding important communications. Pulling is time consuming and inefficient with multiple clicks, memory-based navigation or instruction.
3. Data Processing
Data processing poses a unique set of challenges as well. Extracting meaningful information is tedious. Exploiting the data to facilitate critical decision-making can involve more email, links or attachments. Storing the data encompasses problems with infrastructure, cost, scale and accessibility.
Vision creation and our journey.
Our founder knew there had to be a better way to leverage data in the financial crimes space. His vision began in late 2017 with a basic concept:
Human experts
identify requirements
Human experts
identify credible sources
Human experts
collect data from those sources
Ultimately yielding a consistent, documented, and complete compiling of data.
*Agile product development ramped up in mid-2018 where we began introducing analytic techniques, data science, and ultimately cloud technologies to curate data – making our data unified, uniform, and scalable.
