Big Data Big Business - Achieving advantage through technology

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1 Big Data Big Business - Achieving advantage through technology Prof. Dr. Michael Feindt, Karlsruhe Institute of Technology KIT Chief Scientific Advisor, Blue Yonder GmbH & Co KG European Life & Health Tour, London, October 12, 2012

2 Big Data: Google, Facebook: Unstructured data from the web Map Reduce For many users it means something quite different: Gigantic databases Technology at CERN and other particle accelerators Grid Computing Predictive Analytics, NeuroBayes Data driven decision making in companies Also very interesting for insurances! Statistically relevant insight instead of gut feeling.

3 Predictive Analytics - the IT-topic of the coming years -gigantic value of data stored in data warehouses -optimisation and automatisation of strategic and especially regularly reccurring operative decisons -Predictive Analytics Software uses information in company data bases, combines it with external data sources, and processes it with most modern mathematical methods to calculate predictions about the future, employing probability densities, and on this basis makes optimal decisions.

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5 Big Data at CERN: 40 million collisions per second. 1 PByte= 1015 Byte= bytes of data / second Prof. Dr. Michael Feindt, KIT and Blue Yonder, Swiss Re Life and Health Insurance Tour, London, Oct. 12,

6 Data rates Trigger: Datareduction 1/10 Mio. 1 PB per year are stored and made available to thousands of physicists worldwide: à GRID

7 1 PetaByte = Byte If 1 bit corresponds to one leaf... 1 Pbyte corresponds to all leaves on earth

8 NeuroBayes A high-tech-algorithm from experimental high energy physics can learn complex dependencies from historical data bases of companies and uses this for predictions of the future. Based on an artificial neural network, but is much more. Extremely high generalistion ability (i.e. the future reality is well described by the prognosed probability density.)

9 NeuroBayes in elementary particle physics: Discovery of new particles and new reactions Used in Online-Trigger at LHCb Full automation of complex scientific analyses (KEK: Equivalent of about 500 PhD theses with 72 NeuroBayes networks: efficiency +100% compared to manual work of 400 scientists in 10 years)

10 Knowledge from world-class research in high energy physics CERN, Fermilab, KEK Teilchenkollisionen the largest particle accelators of the world collisions per second. Gigantic data Peta-Bytes of raw data, to be distributed world-wide 1 interesting event per 10 mio collisions. applied to problems in economy Prof. Dr. Michael Feindt, KIT and Blue Yonder, Swiss Re Life and Health Insurance Tour, London, Oct. 12, 2012 Seite 10

11 Stationary, distance-, online trading Better sales predictions and optimised merchandise planning for articles, to improve ability to deliver and less unsold articles at end of season What were if fashion in the right colour and size were never sold out? Page 11

12 1000 stores, 5000 fresh articles Up to 5 mio. sales predictions per day Up to 1.5 billion sales predictions per year Fully automatic optimal purchase quantity calculation per store/article/ day and automatic ordering What, if a large retailer knew exactly how much fruit of each sort were sold which day? Page 12

13 Millions of customers, hundred millions of historic transactions Fair, risk-adjusted tariffs Precise overview over the risk of an insurance What, if an insurance knew individual risks over months and years into the future? Page 13

14 A company with strong brains Founded in 2008, Blue Yonder with its NeuroBayes Suite belongs to the leading vendors of prediction and pattern recognition software or Predictive Analytics.» Unique predictive analytics suite with a combination of statistical algorithms and highly optimised neural nets.» Distinguished and experienced physicists and information scientists from famous research institutes like CERN are the developing team» NeuroBayes has its origin in experimental particle physics and was developed in more than 400 man-years. Seite 14

15 The difference: better algorithms / employees / results Cyberchampion Award Data Mining Cup Top Product Trading Retail Technology Award Bwcon:CyberOne Award Winner High Potentials CyberChampions judges yooung and expanding companies in the technoloy region around Karlsruhe, Winner 2006,,auction prices (,,ebay ) Winner 2009 Sales prediction ( Libri ) Winner 2010 Intelligent Couponing ( Amazon ) After bronze in 2011 readers of the journal handelsjournal voted for Silver for the NeuroBayes in 2012 in the category cost effectiveness Retail technology award: Best Enterprise Solution for Blue- Yonder-Customer OTTO 2012: Blue Yonder most innovative Medium sized/ growth companies in Baden- Württemberg Seite 15

16 Neural Networks The information (the knowledge, die expertise) is in the connections between the nerve cells. Each neuron takes fuzzy decisions (Fuzzy-logic) NeuroBayes > learns extremely fast from historical data (weeksà minutes) > is extremely robust > suppresses statistical noise (high generalisibility) > Can make binary decisions (classify) > Can calculate complete probability densities > Can predict the future reliably

17 Prediction of the complete probability density Expectation value Mode Standard deviation (Volatility) Deviation from normal distibution (heavy tail)

18 Turnaround prediction for distance selling company

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22 The <phi-t> mouse game: or: even your ``free will is predictable //

23 Technology overview: NeuroBayes» System integration ion basis of standard protocols and interfaces» Highly performant, scalable data processing architecture» Handles data in batch or real-time streaming mode» Training at run time possible without degradation of performance Seite 23

24 NeuroBayes applications for insurances

25 e.g. Individual risk predictions for car insurances: Accident probability Claims distribution Large claim prediction Contract cancellation prediction Successfully implemented at

26 Correlations to target variable Ramler II-Plot

27 NeuroBayes constructs risk optimal tariff systems Majority of good customers pays too much and thus subsidize the bad customers not paying enough: 40% pay too much 60% pay too little NeuroBayes adjusts the premium to the individual risk (at constant overall premium) 57% pay less than before 43% pay more Increase of the new NeuroBayes premium by 10%: 50% pay less than before 50% pay more Anzahl Kunden Prämie zu hoch Prämie zu niedrig Anzahl Kunden Anzahl Kunden Risiko/Prämie Prämie, normiert Prämie, normiert

28 NeuroBayes delivers precise prognoses for the customer-individual number and hight of claims Premium differentiation: NeuroBayes adjusts premium to customer-individual risk Customer structure optimisation Bind your good customers and take the bad customers Rentability improvement: Simultaneously increase your total premium volume and decrease your claims rate with a more just tariff system Risiko Premium volume Anzahl Kunden Alter Tarif NeuroBayes Claims rate Bisheriger Tarif Prämie, normiert Alter Tarif NeuroBayes

29 Private health insurance claims per year anything but normally distributed... NeuroBayes has the solution for difficult distributions of type f (t) = (1 " P)# $(t) + P# f (t t > 0)! Many insured persons (fraction1-p) do not generate any claim When there is at least one claim, (fraction P), these are distributed according to f(t t>0). This distribution has fat tails (extremely high claims). t Difficult to handle by classical methods

30 NeuroBayes calculates for each insured person x the individualised Bayesian probability density. NeuroBayes has the solution for difficult distributions of type f (t x) = (1! P( x))"!(t)+ P( x)" f (t t > 0, x ) Insured person x will have no claims with probability 1-P(x) If insured person x will have any claim, the costs will be distributed according to f(t t>0,x) t δ(t) = Dirac- delta-,,function (distribution)

31 Evaluation of prediction methods Typical classical prediction methods e.g. generalised linear models return one value (point estimator). Often the interpretation is not unique. Often the value must be calibrated. Mostly no uncertainty or distribution of the truth around this value are predcicted. NeuroBayes can do more. From probability density economically optimal point estimator can be determined. Often used quality criterion: Mean square deviation or R 2 Not robust for distributions with,,fat tail And economically irrelevant (insurance pays, not 2 ) Quality of prediction results should be evaluated by following criteria: > high individualisation > generalisibility (no overtraining; i.e. individualisation turns out to be correct). >correct prediction of expectation values (Autocalibration). >correct predcition of uncertainty in form of credibility intervals. Much better: Median absolute deviation (MAD)

32 As large as possible individualisation The area between the lift-chart and the diagonal (Gini-coefficient) is as large as possible Gini = 0.41 Gini = 0.32 Sort customers according to predicted risk. Select fraction x with largest predcitions. y= Fraction of cumulated claims in this selection. max Fläche = 0.47 NeuroBayes Prof. Dr. Michael Feindt NeuroBayes-Prognosen als Basis für risikogerechte Wechselmodelle in der PKV classical model The NeuroBayes prediction individualises better! Seite 32

33 Judgement of prediciton methods Good generalisation ability (no overtraining) Gini coefficient (area between lift chart and diagonal) on test sample (green) compatible with expectation of training sample Lift-Chart for true claims Die forbidden regions correspond to a sorting power better than the truth (impossible) or worse than random. Test-Datensatz The area in this case is compapatible wiht the expectation (even a bit better) Trainings-Datensatz Prof. Dr. Michael Feindt NeuroBayes-Prognosen als Basis für risikogerechte Wechselmodelle in der PKV Seite 33

34 Check of calibration (NeuroBayes-diagonal plot) Test-Sample NeuroBayes-expectation value prediction: The mean value of the truth of all insured persons with mean prediction in 100 -bin really is 100 prediction is correct!

35 Quality of prediction Sorting of classical methods seems sensible, there is some correlation. However, without further calibration as prediction of mean value unusable. Red points Mean value of truth in bin Green region contains 68 % of entries with given mean prediction yellow region contains 95 % of entries with given mean Should lie on diagonal Should be as narrow as possible klassisches Modell NeuroBayes Prognose Daten mit Rechnungsbetrag>0 Daten mit Rechnungsbetrag>0 Prof. Dr. Michael Feindt NeuroBayes-Prognosen als Basis für risikogerechte Wechselmodelle in der PKV Seite 35

36 Quality of prediction Test of individual credibility intervals In future we will know the truth. We already now can predict that it will lie in 68 % of all cases in the predicted 1σ-credibility interval in 95 % of all cases in the predicted 2σ-credibility interval Typical test result: Fraction of entries in 1σ-interval : 68% expected Fraction of entries in 2σ-interval : 95% expected 67.9% measured 94.3% measured Tests in many very different applications: NeuroBayes -credibility intervals are very reliable. Most classical methods cannot calculate reliable zuverlässigen confidence or credibility intervals. Experience from NeuroBayes PKV-projekts: Credibility intervals for prediction in 2 years About 9% larger that for next year.

37 Prediction of quantiles is reliable over the complete widths. Multi-quantile-test: Fraction of insured persons in bins of e.g. expected mean costs, whose true claims (future information) will lie below the predicted 30%-, 20%-, 10%-...quantile. Prognostizierte mittlere Kosten The true costs are distributed over the complete width of the predcited probability density just as predicted. The predicted quantiles are reliably reconstructed. Attention: Bayes theorem! If tests are separately performed in subsamples, this separation is not allowed not depend on future information! Allowed are e.g. separation into sex, age, tariff, and any predicted quantities, in short any information known at prediction time.

38 Long-time prediction from anamnesis Target here: Probability that an insured person will claim more than average for his age/sex Simple NeuroBayes text analysis of anamnesis at start of contract (regularised Naive Bayes-ansatz) Measure for classical modelling: Percentage risk loading Comparison of sorting power for different time horizons: Bayes-analysis of anamnesis makes significant predcition even more that 10 years in advance Usual risk loading factors almost no correlation to truth, long term even worse than random Prof. Dr. Michael Feindt NeuroBayes-Prognosen als Basis für risikogerechte Wechselmodelle in der PKV Seite 38

39 Usage of NeuroBayes allows large improvements... Customer-individual claims distribution Probability distributions of indivual insured persons can vary considerably. Sorting powe NeuroBayes successfully sorts insured persons according to expected claims NeuroBayes calculates an individual probability distribution for each single customer. This allows a prediction of all releveant quantiles, thresholds and other statistical quantities. NeuroBayes shows by far best sorting power. The rank correlation coefficient is 3-times larger compare to classical (GLM)- predcition models. rsp: Rangkorrelationskoeffizient nach Spearman

40 Big Data and Predictive Analytics data driven individualisation of reliable predictions is possible using most modern statistical methods and software on large data sets. Prediction of individual risks More justice in tariffs, more profitable and simultaneously for the majority of clients more attractive tariffs. Individual customer scoring Individual optimisation of cross-selling Contract cancellation predictions Churn management Insurances only use a small part of the treasure sleeping in there data bases. Gigantic economic chances!