Meet MIFID II requirements for 10% depreciation reporting while providing added value for clients.
TierDrop is an innovative solution designed to address the complexity of meeting MFID II requirements for 10% discretionary reporting. This independent tool includes ultra-fast calculations, timely alerts and industry recognised calculation methods. Advisors and investment managers can reduce the operational challenges of manual calculations while using results to provide proactive client service.
Automate manual processes and collate all information into the one system. Reduce the risk of error, access information fast and meet regulatory requirements.
Thanks to ultra-fast calculating capacity, advisors can ensure reportable information is available and flagged at the start of each business day.
Proactive portfolio management
Take advantage of ‘Danger Zone’ alerts, with advance warning when client accounts are approaching the 10% depreciation mark. Advisors and investment managers are given time to re-evaluate the portfolio mix and engage with clients before notification.
Enhance the customer proposition
Provide accurate and timely reporting within existing customer communication channels. Set up client email alerts within 24 hours of regulatory requirements and better manage client expectations.
TierDrop is easily integrated with existing systems including adviser portals, back office systems and external reporting engines, ensuring no disruption during implementation.
‘Danger Zone’ reporting
Receive automated notifications when a client reaches the ‘Danger Zone’, highlighting when their portfolio is approaching the 10% depreciation mark.
Industry recognised calculations
Choose from a range of industry recognised calculations including, Internal Rate of Return, Modified Dietz, Simple Dietz, the ESMA suggested method and more.
Automated emails keep clients informed of changes without delay and helps meet regulatory requirements.
Complex calculation capability
Ability to handle complex investor scenarios, such as multiple model switches within a calculation period, and identify how changes in underlying data impact performance.
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