The Competitive Edge. Fraud detection and proactive risk management. Brasov May 28th, 2014

Transcription

1 The Competitive Edge Fraud detection and proactive risk management Brasov May 28th, 2014

2 Market Trends Growing existing portfolios Establishing direct, online insurance channels STP (straight-through-processing) used for streamlining existing insurance processes Improving customer satisfaction/retention rates Evaluating and updating your risk management and fraud detection What if you could lower your combined ratio by up to 5%? What if you could effectively reduce the risk at underwriting of frequent claimers on online channels by 20%? What if you could streamline your current risk practices? What if you could improve customer satisfaction/retention by not passing on higher premiums resulting from loss due to undetected fraud?

3 What are the signs your company is ready? The authorities have given us some serious warnings about our compliancy screening We want to show our clients and society that we are fighting fraud better than our competitors Our combined ratio is growing and is almost hitting the 100% We have a fraud detection system but have many false positives and changing the rules is very hard and expensive We want to make our processes more straight through We are recently faced with large fraudulent incidents and just don t seem to detect them Since the introduction of our online channel our loss ratios are going through the roof

4 Where do we begin? In order to know your savings potential we need to measure the following concerning your manual investigations: What is your current detection rate? What is your present success rate?

5 Apply the Hybrid model FRISS Detection Expert Model Profile Model Predictive Model Social Network Analysis Method based on knowledge rules Method based on "Normal behavior" in profiles Predictive models based on historical data Networks based on Link Analysis FRISS score

6 External Data Internal Data Fraud Detection What are the trends? Hybrid Approach Solutions for Fraud Detection Existing in Future Rules Filter fraudulent transactions Examples: Claims in Rules short time period; claim within short period of renewal Database searching & Contributory databases Search database / web services Use matching techniques Verify data over multiple databases external data sources Contributory Contributory databases Examples: watchlist, known fraudsters, external verifcation data Anomaly Detection Detect individual and aggregated abnormal patterns vs. peer groups Examples: Anomaly Detection Deviation, clustering, univeriate & multivariate regression. Sequence analysis, peer group analysis Text Mining Look for patterns in unstructured data, documents, blogs, reports Examples: Text Scripted words Mining or phrases, multiple claimants using same words or phrases; specific phrases suggesting lying, Advanced Analytics Perform knowledge discovery, data mining, predictive analytics Trend analysis, Advanced Time Series Analytics Analysis Examples: Neural networks, decision trees, generalized linear models, gradient boosting Link Analysis Look for unexpected relationships Examples: Link Social network Analysis + linkage analysis + community detection + advanced analytics Photo Manipulation Automated screening on images Photo Manipulation Examples: EXIF and manipulation photos of damaged cars or PDFs Image screening Pro active data alerts Receive data alerts from external data sources, devices, Pro telematics, active Data Harvesters data alerts Examples: Vehicle data vendors send alerts when car has been sold Voice Detection Voice stress analysis Voice part of claim intake Detection Examples: check lies, exaggeration of claim Behaviour detection Online Device Online Screening Device Online Screening fraudulent devices Cross border searches Cross border More In depth screening searches but to detect cross border fraud Known Patterns Known Fraud Unknown Patterns Unstructured Patterns Complex Patterns Associative Link Patterns External Data Monitoring International fraud Patterns

7 FRISS Funnel Explanation Total Number of Claims Automated Detection rate: #hits / #claims Hits After Manual investigation by sr. claim handler fraud suspects: #suspects for investigators/#hits Manual Investigation success rate: #proven frauds/#suspects Fraud suspects Proven frauds Success rate of the hits Success rate of the claims

8 Measure results before and after Total Number of Claims Automated Detection rate: #hits / #claims DETECTION BASED ON HUMAN INTELLIGENCE AUTOMATED DETECTION Hits After Manual investigation by sr. claim handler fraud suspects: #suspects for investigators/#hits Manual Investigation success rate: #proven frauds/#suspects Less cases to be investigated 67% Fraud suspects New detected cases 43% Proven frauds Success rate of the hits Success rate of the claims

9 Sharing knowledge & information Friss Learning Cycle Friss Learning Cycle Friss Learning Cycle Reject or quote according to risk Pro Active Alerts to prevent high risk claims Overall score, input for claim processing Quotation & Underwriting Policy Change Claim Handling Real-time screening Automate Add watchlist, application Build screening predictive and models risk and profiling. profiles for Reduce frequent claim time models, spent historic on underwriting fraud model, activities, area alerts, minimizing trends,.., fraud and safeguarding in order to prevent bad risks portfolio value and fraudulent behaviour entering the portfolio Realtime screening on changes in policy Frequent pro active monitoring check during on Policy current Management policy for example after changing the contract/policy such as: or Address or move alert, insured vehicle vehicle status alerts, insured person alerts, company bankruptcy alert Real-time / batch screening In order Lean on to or significantly integrate previous reduce alerts claims and costs risk and profiles enabling from Underwriting fast track and claim Policy handling, Change to the claim screening and therefore improving effectivily screen, validate combined ratio and process (or investigate) the claim. FRISS Intelligence DB

10 Number of policies Loss ratio Predictive underwriting Use profiles and predictive models based on internal and external data about object, insured, driver, address, phone, bank account, device, etc. Predict future risks (like fast claiming, claim consciousness, fraud risk, etc.) For example frequent claim prediction Frequent claim probability

11 Connecting data & cases Real Time Network detection will reveal hidden relations between subjects and objects (i.e. Phone numbers, bank accounts, devices,...) Proven fraud case New SUSPECT fraud case Proven fraud case Proven fraud case Proven fraud case

12 Network visualization 2 Normal claims FRISS helps to identify 3 unique clusters within the network Fraud ring 3 1 Rental company Which type of cluster do you find most threatening to your business?

13 Measure benefits for your business in combined ratio / loss ratio improvement Proven in a retro analysis for a contributory database (sharing claims & incidents) Argument Score #Policies Loss ratio Person living on the same address as policy % holder has his/her drivers license revoked Claim History on Policy holder: 2 claims for % Motor in 1 year Policy holder is registered on a fraud case % Person living on the same address as policy % holder is registered on a fraud case. Claim History on Policy holder: 5 claims for % Motor in 2 year Claim History on Policy holder: 1 liability claims % for Motor in 1 year Claim History on Policy holder: 2 claims for Home fire in 1 year %

14 Use external data in an innovative way - deeplinking data and proactive alerts - Administration Management Administration Management Principals Principals Principals Personal bankruptcy Company Holding Holding Administration Management Principals Unfavourable out of business Company Company Subsidiary Subsidiary Subsidiary Subsidiary

15 Text mining on Claim Report The power of incorporating unstructured data Reason of claims(hadise_notu) #blocked# GÜNÜ HALİT YILDIZ İDARESİNDEKİ 41 TK #blocked# 243 PLAKALI ARACI İLE 1. CADDE ÜZERİNDE SEYİR HALİNDEYKEN ARACINI SAGA KIRMASI SONUCVU #blocked# PARK HALİNDE OLAN FLEETCORP #blocked# OPERASYONEL ADINA KAYITLI 34 HL #blocked# 2700 PLAKALI ARACA ÇARPARAK HASARA SEBEBİYET VERDİGİ EKTEKİ TUTANAK TETKİKİNDEN ANLAŞILMIŞTIR. Results of expert(eksper_notu) Text Mining on Claim Adjuster Reports will reveal new fraud cases #blocked# 34 HL 2700 PLAKALI ARAC TARAFIMCA GÖRÜLÜP EKSPERTİZ ÇALIŞMASI YAPILMIŞTIR. ANCAK GEREK MAGDUR ARAC GEREK SİGORTALI ARAC İNCELENİP ANLAŞMALI TUTANAK that previously VE HASAR İNCELENDİGİNDE were not detected. HASARIN EKTEKİ TUTANAK DA BAHSEDİLDİGİ ŞEKİLDE OLUŞAMAYACAGI GÖRÜŞÜNE VARILMIŞTIR; ŞÖYLEKİ 41 #blocked# TK 243 PLAKALI ARACIN ARKA TAMPON İLE 34 HL 2700 PLAKALI ARAC ÖN KISMINA ÇARPMASI SONUCU ARACLARIN YÜKSEKLİKLERİDE GÖZ ÖNÜNDE BULUNDURULDUGUNDA ÇARPMIŞ OLSA DAHİ BU ŞEKİLDE HASAR OLUŞAMAYACAGI VE UYUMSUZ OLDUGU GÖRÜŞÜNE VARILMIŞ KALDIKİ SÖZ KONUSU ARACIN PARK HALİNDE OLDUGU ANLAŞMALI TUTANAKDA BELİRTİLMİŞ OLUP DOSYANIN TEDBİR AMAÇLI OLARAK ARAŞTIRMAYA VERİLMESİ TARAFIMCA UYGUN GÖRÜLMÜŞTÜR. ANCAK ARAC SAHİBİNİN BU HASARINDAN VAZGEÇTİ İÇİN DOSYANIN KAPATILMASIAN KARAR VERİLMİŞ VE FERAGAT YAZISI SİSTEME YÜKLENMİŞTİR. UYUMSUZ = Incompatable, ARAŞTIRMAYA = investigation department, FERAGAT = waiver

16 DEVICE REPUTATION COMPONENTS Is this device making a fraudulent 2. EVIDENCE transaction? 1. IDENTIFICATION 3. ASSOCIATIONS 4. ANOMALIES Has anyone seen this device? Has anyone had a bad experience? Does the device have connections? Have any anomalies been found? This round-trip takes about 500 milliseconds! COPYRIGH T IOVATIO N 19

17 CASE STUDY: INSURANCE FRAUD RING Policy IP Address Gloucester Oldham Manchester Fraud Aberdeen LONDON Birmingham Edinburgh Stockport The use of iovation s Device Reputation technology enabled a large motor insurer to link multiple policies together when no static data was related. The policy identified as fraud had a claim value of 54,000. By linking the other policies together the insurer was able to cancel the other grouped policies before further claims were made COPYRIGH T IOVATIO N 20

18 In summary Review your effectiveness Review data quality Integrate throughout insurance processes Use predictive modelling in underwriting Utilize real-time network detection next to social network analysis Combine data from different sources and use proactive alerts on your policies Use the power of incorporating unstructured data with text mining Use intelligence to make your claim process straight through Prepare for online anonimity

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20 Thank you for your attention! For questions please do not hesitate.. Leetha Spyropoulou International Sales Manager Web: Phone: