Evooq

Every private bank is different. Their systems, data models, policies and ways of working have been shaped over years. The obvious solution is to customize the wealth platform around them. But then every implementation becomes its own product. Updates get harder, features ship slower and complexity compounds. We chose a different path. Evooq Wealth stays standard. Banks adapt it through configuration, while a dedicated integration layer connects it to their existing environment. That layer translates how your bank describes clients, portfolios, instruments and the classifications behind them into what Evooq Wealth needs and translates everything back into the systems and workflows your teams already use. Most of that translation doesn't start from a blank page. We’ve often already built and proven the underlying connections with similar core systems. Which is also why implementation moves faster than this industry has learned to expect. This seemingly small architectural decision has a disproportionate consequence: Evooq Wealth can keep getting better for every client at once, while adapting deeply to each bank.

Imagine two colleagues who commute separately to work. Anna is late about 20% of the time, Bruno about 30%. You've tracked both for years and you know each pattern well. Now someone asks: what's the chance they're both seriously late on the same day? You can't answer this from what you know about each of them individually. You need something extra — the structure of their relationship. Do they travel through the same part of the city? When a disruption hits, does it reach both routes? When Anna is having her worst morning, does that make Bruno's worse too or are their commutes essentially unrelated? The same logic applies to a portfolio. A risk model can forecast each asset's behavior correctly in isolation and still be wrong about what matters most in a crisis: whether losses arrive together. And they tend to. In financial crises, assets that moved somewhat independently in calm markets start moving in lockstep when conditions deteriorate — right when the diversification you counted on matters most. Testing risk one asset at a time can't catch this. Here's what does: https://lnkd.in/eHf3G82t

We built Elus to strip the friction out of trading structured products, so the process moves as one continuous flow instead of a maze of calls, emails and spreadsheets. Refined with issuers and buyers who live in this market every day, Elus is the simpler, fairer way to design, price, execute and manage structured products — from first idea to final redemption. Design the payoff. Get live, comparable pricing from multiple top-tier issuers in seconds. Execute with one click or run a full RFQ for something more complex. Then let Elus watch the barriers, autocalls and redemptions for you and only speak up when something actually needs your attention. Want a deeper look? https://evooq.com/elus

A straight line is convenient. If an investment loses 5% when the market falls 5%, it is tempting to assume it will lose 10% when the market falls 10%. For many instruments, that holds up. But for structured products, it can be dangerously misleading. Barriers and other conditions can change the payoff once a certain level is reached. A small additional move in the market can therefore produce a very different outcome. This is the problem with linear approximations in risk. Edgelab fully reprices each instrument under each scenario, accounting for the actual payoff structure rather than extending an approximation. Because when you stress a portfolio, you want to see how the investments actually behave. Not how a straight line says they should. Read more: https://lnkd.in/ev3peeV7

Good advice comes with a surprising amount of work attached. Monitoring, portfolio construction, proposals, risk, compliance, documentation and more. Evooq Wealth takes care of much of the heavy lifting behind the scenes, leaving Advisors more time for the part that still needs a person. Have a look: https://lnkd.in/eRsufsfH

A risk model can be right on average and wrong when it matters. Imagine a weather app that correctly predicts the total number of rainy days each year but only warns you after the rain has started. Its annual statistics might look excellent. You would still stop trusting it. Something similar can happen with risk models. Standard back-testing often asks whether a model produced the expected number of exceptions over a long period. But a model can pass that test while remaining slow to recognise that market conditions have changed. To examine this closely, Edgelab developed a more demanding test: the Tile Test. Instead of looking only at the aggregate result, it checks a model's performance across many time windows and probability levels at once. The finding: several widely-used VaR methodologies that pass conventional back-tests still lag once conditions actually shift. A risk estimate that lags reality can cost clients money at exactly the wrong time. So the question worth asking isn't just "does the model pass the back-test." It's: when markets change, how fast does it actually notice? We break it down here: https://lnkd.in/eM_r_FU7