FAQs

SqSave is a digital investment manager and the consumer brand of Singapore-incorporated PIVOT Fintech Pte. Ltd. (“PIVOT”) (UEN 201716150D). PIVOT is regulated by the Monetary Authority of Singapore and holds a Capital Market Services Licence (CMS 100806).

SqSave's backers primarily consist of angel investors attracted to the fintech solutions and strong leadership, led by founder Victor Lye, who brings over 25 years of experience in investments, insurance, and healthcare.

SqSave designs personalized investment portfolio(s) matched to your risk profile when you ask for it. We manage your portfolio(s) for you on an on-going basis. You don't have to make any investment decisions. Simply decide how much risk you want to take and how much to invest. We do the rest.

SqSave uses machine learning AI to design and manage globally diversified investment portfolio(s) customized for every investor.

SqSave's investment strategy is based on the investment philosophy of Nobel Prize winning Markowitz's Modern Portfolio Theory ("MPT"). Using the MPT framework, SqSave's proprietary algorithms and statistical models uses real-time data to predict a portfolio diversifed across different investments that generates the highest possible return for the given risk exposure.

SqSave's technology involves training our algorithms to progressively improve their predictive power by using "training data" comprised of actual outcomes, mathematical optimization and data analytics. The algorithms adopt machine learning AI techniques to correct its "mistakes" by comparing its predictions against live data outcomes in real-time, adjusting its computational variables and predicting again, all in rapid succession, without being explicitly programmed to do so. That's machine learning.

Machine learning ("ML") is the science of getting computers to act without being explicitly programmed. ML has become so common that you are already using it. For example, in spam-email filtering, online language translation, self-driving cars, speech recognition, web search, and more. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.

SqSave has two main proprietary technologies, namely (i) SqSave Risk Profiler; and (ii) SqSave Digital Asset Allocation System.

Our SqSave Risk Profiler adopts Game Theory which focuses on risk-reward choices and behaviour. We believe that this is an improvement over the traditional questionnaire approach which is not scientifically validated. Moreover, respondents may not understand the technical nature of the questions. Our SqSave Risk Profiler simply requires you to make a series of risk-return decision(s). By analysing the risk you accepted for the desired return, and the sequence of your decisions with respect to the random results, and the actions of others with similar demographics, our SqSave Risk Profiler builds on its AI capability to predict your risk profile.

Our SqSave Digital Asset Allocation System ("DAAS") uses the principles of Nobel Prize winning Markowitz's Modern Portfolio Theory ("MPT") to drive SqSave's proprietary algorithms with real-time data to predict a diversified investment portfolio whose risk-return outcome fits your personal risk profile identified by our SqSave Risk Profiler or as determined by you.

The DAAS involves (i) Data Analytics (ii) Portfolio Accounting System and (iii) Trade Optimisation. Our proprietary Data Analytics system makes sense of real-time data which feeds into our SqSave Portfolio Accounting System to generate the recommended portfolios based on MPT, and the trade orders, as necessary. The Trade Optimisation Module accounts for market friction costs such as execution commissions, fees, bank charges, etc, with the aim of structuring efficient investment decisions to achieve a lower friction cost than if you were to execute the transactions personally.

SqSave focuses on achieving good risk-adjusted returns by predicting market trends across different asset classes to minimize portfolio volatility over the medium to long-term.

SqSave differs from typical "robo-advisers" in two main aspects:

(i) Portfolio design
(ii) Investment management

(i) "Robo-advisers" are typically "digital shopfront, human kitchen" in nature, where investment decisions are ultimately made by human managers. As such, robo-adviser "human kitchens" offer "pre-made" model investment portfolios to match your risk profile. In contrast, SqSave is a “digital shopfront, digital kitchen” system using machine learning AI without human bias. SqSave designs your investment portfolios that matches your risk profile - only when you ask for it - using real-time market data and risk-return predictions.

(ii) When it comes to managing your investments, SqSave is forward-looking. Everyday, SqSave assesses real-time data and predicts several risk-return scenarios that match your personal risk profile. Portfolio rebalancing is done on a "per investor" basis, and only if SqSave predicts that a particular predicted risk-return outcome will be better than your current portfolio. In contrast, typical robo-adviser "human kitchens" rebalance your investments "backwards" to its "pre-made" model portfolios, which may no longer be relevant due to constant changes in market conditions.

Of course! We humans should do what we do better than machines. And let the machines do what humans cannot do better. Our human Squirrels will focus on customer support and making sure things work as planned. Our investment team which designed the algorithms monitor and assess performance everyday to ensure that the overall system works as planned.

SqSave holds a Capital Markets Services license and commenced operations in early 2019. Since then, SqSave's machine learning AI technology, based on the MPT investment framework, has been commercially deployed, showcasing a track record of over five years.

SqSave's technology has been adapted and deployed at various financial institutions across ASEAN.

If you want to invest (grow your savings with a measure of risk over a time frame of over one year), then SqSave will work for you. SqSave harnesses the power of AI to manage your investment risk in real-time with consistency, which is humanly not possible. Humans get emotional, need time out and cannot cope with voluminous data. Machines can do the work consistently without rest, can see patterns that we don't even recognise.

Global investment markets keep moving as one part of the world goes to sleep and another part starts its workday. Tracking investment risks must cover huge volumes of data in real-time, all the time. Computing power has advanced tremendously. Hence, machines can manage risk consistently over the medium to long term. Humans will be affected by emotions and cannot process voluminous data efficiently.

SqSave's investment AI team is led by Victor Lye with over 25 years' leadership experience in investments, insurance and healthcare. He has led research, stockbroking, corporate finance, asset management, private banking, insurance and healthcare. Most of all, he has seen investment cycles since the 1980s. Victor founded SqSave in the belief that machine learning AI is better than traditional investment methods that charge clients too much.