Catena Technologies' survey revealed that 80% of the market is extremely concerned about the quality of the OTC derivatives data being reported to the regulators. Thirty-eight percent of respondents highlighted that position reconciliation is one of the most challenging areas for trade reporting. However, more than 90% of the participants had yet to fully automate reconciliation processes across all asset classes. Sixty-three participants responded to the survey. Participants included global banks, regional banks, and various buy-side firms from Singapore, Australia, India, Hong Kong, China, Japan, Canada, the US.
The survey also revealed that 54% of respondents believe that the greatest challenge is understanding current trade reporting requirements and adapting to new requirements. Universal trade identifier (UTI) pair and share reporting was an area of particular concern. Thirty-four percent of respondents indicated that they use electronic platforms to generate UTIs for at least some transactions, but more than half of the participants reported that they manually generate UTIs. Forty-nine percent reported that they face challenges preparing for the UTI pair and share workflow, which includes generation and communication of UTIs to counterparties, as well as the receipt and reporting of UTIs generated by other counterparts.
Aaron Hallmark, CEO of Catena said: "Many firms that deployed tactical solutions to meet compliance deadlines are now looking for strategic solutions that can improve their reporting efficiency, adapt to business growth and new requirements, as well as address data quality concerns. Catena is committed to help firms tackle these challenges."
"Catena's research validates what we have been hearing at DTCC: Market participants are placing more focus on the controls and reconciliations around reporting, with a goal of maintaining 100% compliance." commented Peter Tierney, Head of DTCC's GTR Asia, "Regulators are starting to use the data in its current form, and early stage analysis is driving an increased focus on data quality. Greater analysis and usage will drive higher data quality, which in turn will enable better analysis."