Wealth managers are faced continuously with client suitability concerns. They need to determine whether a particular investment decision is in the best interest of the customer, as well as to comply with ever-evolving regulations. This requires identifying, capturing, reviewing, tracking, and refreshing records including Individual client product and service preferences and restrictions, long and short-term objectives, targeted goals and time horizons, sophistication level and risk appetite records, and investment restrictions (such as cross border rules, tax issues). All of this is quite challenging. In addition, navigating and complying with a myriad of geographically-specific but often divergent suitability regulations (FINRA, Dodd-Frank, MiFID II, EC Sustainable Finance regulations, etc.) adds complexity, increases liability exposure, and is time-consuming as well as costly.
Synechron’s Wealth Tech Accelerator for Efficient Suitability utilizes data analytics as well as Artificial Intelligence / Machine Learning to allow rules and boundaries to be defined and altered on the fly by both the client and the bank. It does this by integrating several core elements including a client’s defined risk classification and portfolio security holdings/positions, asset/sector type and geography needs, client preferences (Environmental, Social and Governance criteria, for example), and adherence to suitability rules and legal compliance. In addition, it incorporates event-driven metrics which can identify changes in client risk appetite before a trade is permitted. It also uses historical trade monitoring to signal a departure from known client trading patterns. It not only recognizes and actively manages potential suitability conflicts, unwarranted recommendations and unsuitable investments, but it also assists a bank’s investment personnel by proactively screening a given investment universe for suitable investments and recommendations. Additionally, it neatly integrates with a bank’s existing systems (core banking, product, trading systems etc.) and operates like a microservice on top of these systems for an easily-implemented, light-weight infrastructure enhancement.