Who we are Founded in 2011, we are now 16 people based in Sweden and the UK Our clients are banks and insurance companies We focus on computational science with financial applications
Our services Advisory services Managed services Analytics We provide advisory services Pluggable toolbox of Standardised risk covering a wide range of services or Process-as-a- management Software-as-a- financial applications and Service (PaaS) to help Service (SaaS) components aspects: financial institutions execute Product rank – on their business models Risk models automated advice more efficiently including end customer Pricing and valuation Cost efficient cloud-based risk profiling solutions Risk & business insights Mitigation of key-person Regulatory expertise risk 3
Our Key Competences F I N A N C E & I N S U R A N C E Our consultants have a strong skill set backed by an extensive track record Risk management • ALM • Asset management • M A T H E M A T I C S • Regulatory frameworks F I N A N C E We are skilled in mathematical modelling T E C H N O L O G Y • Risk models • Valuation We have designed and A G I L E Regression & ML • L E A D E R S H I P built a number of large Model validation • systems Calculation engines • T E C H M A T H S Integration platforms • • Data quality frameworks • Automated processes
Our background Zaliia a Gindullina ina Sanna Brande del Business Developer Analyst zaliia.gindullina@kidbrooke.com sanna.brandel@kidbrooke.com M.Sc. Accounting and Financial M.Sc. Mathematical statistics, Management Economics coursework Stockholm School of Economics Lund University M.Sc. Financial Markets and Financial Institutions Higher School of Economics 5
Our mission We democratise risk management
Case study Automated Financial Advice
Background Business challenges New regulatory requirements drive change Decreasing willingness to pay for investment management Financial firms express demand for cost-efficient multi- channel and digitised customer journeys
Regulations Increased transparency requirement w.r.t product cost Necessary to monitor: Revenue streams + Product fee structures + Investment firms required to specify value adding services with respect to end customers Prioritised focus area for Swedish FCA throughout 2018 + Continuous prevention of possible conflicts of interest required
Willingness to pay Relentless focus on price Launch of Avanza Global < 10 bps fee + Some end customer segments are less hesitant than ever to switch their financial advisor based on cost Building trust to counter decreased willingness to pay is more important than ever
Case Major Swedish Life-Insurer and Unit-Linked Platform Three year roadmap for digital advice Starting with digital pension advice + End state will be full customer customer balance sheet advice + Core risk and advice platform based on third party economic scenario generator and Kidbrooke “Product Rank” Model maintenance managed by Kidbrooke
How it works Could be purpose-built by our consultants or delivered via SaaS Typically provided in-house or by a third party Digital advice Kidbrooke Advisory SaaS or Managed Services Answers to risk Calibrated utility Risk profile profile questions function ESG Scenario-set Answers to savings Savings Goal goal questions Initial and monthly List of products investment amounts and investments Utility evaluation Answers to personal Financial profile ranked according finance questions to utility Product/fund universe • Figures/levels used in The savings goal and • Utility is evaluated for • The ranked products can • The risk profile is • each product and optionally be subjected risk profile questions financial profile represented via parameter investment combination to further deterministic are calibrated using information is combined values of the utility function the scenario-set and over all scenarios in the constraints before to recommend suitable The utility function itself • general levels of scenario-set advising the customer to investment amounts and calibration of its wealth The risk profile can be • Real time portfolio choose the investment • parameters can be adapted • Compatible with a extended with construction w.r.t. utility with the highest utility to suit your needs also possible number of leading information about how • The selection itself and ESGs (e.g. Numerix, actively a customer attributes of the Ortec Finance, wants to monitor and investments (fees, Moody’s Analytics) manage his or her assumptions about value investments add, etc.) can easily be Risk profile calibration • reviewed using the product can be adapted to rank functionality existing sets of risk profile questions
An advanced approach to risk profiling Why is this important to a The Industry Standard The Kidbrooke State-of-the-art financial firm? Customers gain or lose points for each question assessing Our advanced risk profiling methodology allows for their risk appetite. These are later summed up with little distinguishing between a larger number of risk profiles. More granular approach to risk regard to the nature of the questions and therefore We assess the possible combinations of answers appetite assessment enhances peoples’ underlying attitude to risk separately and therefore we achieve a more consistent the quality of advice and accurate risk profile Example How do we do this? Risk Question I – Risk Tolerance Aspect I • a) Low Risk Level Answer – 4 points 1. We introduce risk tolerance intervals as the underlying b) Middle Risk Level Answer – 2 points result of the answer to each question; c) High Risk Level Answer – 0 points 2. We consider the consistency of the customers’ Risk Question II - Risk Tolerance Aspect II • answers and weight the responses in accordance to Fast track to customer a) Low Risk Level Answer - 4 points the nature of the question; satisfaction b) Middle Risk Level Answer – 2 points 3. We calculate the individual risk tolerance weighting c) High Risk Level Answer - 0 points and summing up the results of responses, capturing Total Risk Aversion Level = Sum of Risk Points the unique client risk appetite accurately. If a customer selects a low risk level for Question I and a Bottom line: high risk level for Question II, which address different aspects of the risk profiling; the points system will not Sustainable value creation Individual Enhanced Consistent distinguish this customer from the one choosing a middle approach to end approach to risk treatment of risk level for both Questions customers profiling risk
Comparative study Least-Squares Monte Carlo vs. Artificial Neural Networks
Least-squares Monte Carlo A 2-step procedure 1. Nested Monte Carlo simulation of outer and inner scenarios a) a) Outer ter scen enari rios os Generated under real world measure b) b) Inner er scen enari rios os Generated under risk neutral measure, used to valuate each instrument conditional on the generated risk factors. Scenario value = averaged inner scenarios 16
Least-squares Monte Carlo A 2-step procedure 1. Nested Monte Carlo simulation of outer and inner scenarios a) a) Outer ter scen enari rios os Generated under real world measure b) b) Inner er scen enari rios os Generated under risk neutral measure, used to valuate each instrument conditional on the generated risk factors Scenario value = averaged inner scenarios 2. Least-squares regression over averaged inner scenarios ⇒ obtain LSMC proxy function 17
LSMC - VaR of European put option Value-at-Risk (VaR): 𝑊𝑏𝑆 𝛽 = 1− 𝛽 -percentile of return distribution, or 𝛽 -percentile of loss distribution: Fig: VaR of demeaned return distribution.
LSMC - VaR of European put option Step 1: Nested simulation Outer scenarios: 𝑛 up until 𝑢 𝑝𝑣𝑢𝑓𝑠 = Simulate 𝑛 = 1, … , 𝑂 𝑝𝑣𝑢𝑓𝑠 stock process 𝑇 𝑢 1 year. Inner scenarios: Starting from each outer scenario, simulate 𝑜 = 1, … , 𝑂 𝑗𝑜𝑜𝑓𝑠 inner stock processes ( 𝑂 𝑗𝑜𝑜𝑓𝑠 << 𝑂 𝑝𝑣𝑢𝑓𝑠 ) up until 𝑢 𝑗𝑜𝑜𝑓𝑠 . Put option value: 𝑂 𝑗𝑜𝑜𝑓𝑠 1 𝑢𝑗𝑜𝑜𝑓𝑠 𝑠 𝑡 𝑒𝑡 max(𝐿 − 𝑇 𝑢 𝑗𝑜𝑜𝑓𝑠 𝑓 − 𝑛 𝑜 𝜌 𝑢 𝑝𝑣𝑢𝑓𝑠 , 𝑇 𝑢 𝑗𝑜𝑜𝑓𝑠 , 𝐿 = 𝑢𝑝𝑣𝑢𝑓𝑠 , 0) 𝑂 𝑗𝑜𝑜𝑓𝑠 𝑜=1
LSMC - VaR of European put option Step 2: Least-squares regression 𝑛 𝑍 = 𝜌 𝑢 𝑝𝑣𝑢𝑓𝑠 , 𝑇 𝑢 𝑗𝑜𝑜𝑓𝑠 , 𝐿 , 𝑛 = 1, … 𝑂 𝑝𝑣𝑢𝑓𝑠 1 , 2 , … ] 𝑛 𝑛 𝑌 = 1, (𝑇 𝑢 𝑝𝑣𝑢𝑓𝑠 𝑇 𝑢 𝑝𝑣𝑢𝑓𝑠 −1 𝑌 𝑈 𝑍 𝑍 = 𝑌𝛾 + 𝜗 ⇒ መ 𝛾 = 𝑌 𝑈 𝑌 LSMC proxy function: 𝑔( መ 𝑛 • 𝛾, 𝑇 𝑢 𝑝𝑣𝑢𝑓𝑠 ) • No inner scenarios required: , 𝐿 = 𝑔( መ 𝑛 𝑛 𝜌 𝑢 𝑝𝑣𝑢𝑓𝑠 , 𝑇 𝑢 𝑗𝑜𝑜𝑓𝑠 ො 𝛾, 𝑇 𝑢 𝑝𝑣𝑢𝑓𝑠 ) VaR obtained from quantiles of option value • distibution.
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