Date: Oct 21 2021
Private Wealth Systems, a global financial technology company that is revolutionizing the way private wealth is analyzed, reported, and managed. Private Wealth Systems has been named a FinTech Top 20 Company by American Banker, and Top FinTech Innovator by CIO Review.
For decades, investors had to sacrifice accuracy for operating scale. The choice was always between hiring multiple people to perform manual data entry and verification checks to ensure tax-level accuracy or go without and suffer major data errors. This false choice has been a structural challenge in the FinServ and FinTech industries up until now.
As sophisticated investors around the world increase their allocation to private equity, venture investing, and real estate, through direct investing and third-party funds, there is a growing awareness of the challenges that exist in aggregating, reconciling, and producing total wealth reporting and analysis that is not only accurate but operationally efficient. Of greater importance, investors who question the data from existing reporting systems are realizing the size and consistency of the errors that most financial software systems produce across liquidity, gain/loss, and performance calculations.
This is best illustrated with a live example from a multi-family office that has been using a well-known fintech reporting platform, “[our current provider] is off by 230 bps on a single investment.” These errors make it impossible to correctly analyze which managers, mandates, and investment strategies are successful and which are not. Errors also create both regulatory risk and reputational risk.
Although there are multiple root-causes of data errors, a few large contributors include:
- Industry Challenge #1. Private investments held inside brokerage accounts. When a cash contribution is made to a private investment, the bank may have up to a twomonth lag in updating the private investment value. During this lag time. investors risk reporting erroneous data on liquidity, gain/loss, performance and exposure. For investors that allocate a majority of their portfolios to alternative assets, these errors can have serious consequences on investment decision making.
- Industry Challenge #2. Duplication of cash flows. Private managers often send interim notices, such as a capital call. The information on the capital call will also be included in the monthly or quarterly statement. Most systems, especially AI powered, simply duplicate that data, and pass those errors onto investors.
- Industry Challenge #3. Phantom Price/Shares. Most custodians and legacy fintech platforms weren’t built to support private investments. Therefore, they force users to create price and shares for an instrument that doesn’t have price or shares. For example, most systems don’t allow you to invest $5 million into XYZ Fund IV, you have to create a phantom price of $1 and then buy five million phantom shares. Every time the investment has an update, like a contribution or distribution or valuation, you need to adjust both price and shares. Having to make proper adjustments is a major challenge in the industry, and often generates doubledigit data errors.
To eliminate data errors and the operational burden of manual intervention, a system must be built from scratch with the intended purpose to solve the structural data challenges that continue to impact the family office and private wealth industry. This means a system must have a different approach to data aggregation, accounting, and performance calculation, focusing on capturing every component of every investment flow with an automated and manual reconciliation process that identify errors based on instrument type, custodian or manager, and global jurisdiction. The system must have foundational logic to automatically process changes in cost, cash, accruals, and performance based on the type of transaction and the timing of the transaction with a single transaction entry to ensure its one-and-done. There must be automated checks in place to ensure all of the investment activity matches and is correct, even when the bank or manager is reporting errors, which happens often. There must also be an operations team that when needed can manually adjust transactions to ensure tax-level accuracy on a consistent basis.
All of this leads to the real goal of solving the family office industry’s structural challenges by blending experience and understanding of the family office ecosystem with meaningful innovation in modern transaction processing technologies such as logic-based transaction codes that automatically process multiple data points with single entries to ensure timely, accurate data for the industry.