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Digital Crime Scene Investigation
The document discusses Inspirient GmbH's hybrid AI system for fraud detection and prevention, highlighting its capabilities in automated data analysis, anomaly detection, and real-time response. It outlines the challenges of traditional fraud detection methods and emphasizes the need for a hybrid approach that combines rule-based and machine learning techniques. Additionally, it presents real-world applications and success stories demonstrating the effectiveness of their solutions in various sectors, particularly in banking.
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Digital Crime Scene Investigation
- 1. Copyright © 2022 by Inspirient GmbH. All rights reserved. www.inspirient.com /company/inspirient @inspirient Digital Crime SceneInvestigation Dr. Georg Wittenburg ▪ Inspirient GmbH Sicherheitskooperation CyberCrime 2022 Mainz ▪ 31 May 2022 Winner BNP Paribas International Hackathon 2017 Winner BARC Startup Award 2016 Winner Startups@Reeperbahn 2017 Top 10 NATO ACT Innovation Challenge 2018 Top 10 Deloitte RegTech Universe 2019 Selected for 2021/22 AI German Landscape Trusted AI Startup 2020 European AI Startup Landscape
- 2. Copyright © 2022 by Inspirient GmbH. All rights reserved. 1 Source: BNP Paribas/ Social Media Celebrating with Consorsbank / BNP Paribas Risk Team Sample alert (sanitized) 31.05.2022
- 3. Copyright © 2022 by Inspirient GmbH. All rights reserved. Agenda Fraud Detection withAI in Four Steps • Automated Data Analysis • AI-supported Anomaly Detection • The Human Factor • Real-time Fraud Detection 31.05.2022 2
- 4. Copyright © 2022 by Inspirient GmbH. All rights reserved. The Crime Scene 3 Definition,for this talk • Crime scene is digital, but not organized • Data is structured but not consolidated, with varying data quality Examples • Business data on a laptop • Data dumps from corporate IT • Externally acquired, gray market dataset 31.05.2022
- 5. Copyright © 2022 by Inspirient GmbH. All rights reserved. Manual Search isnot an Option 31.05.2022 4
- 6. Copyright © 2022 by Inspirient GmbH. All rights reserved. Automated Analytics 31.05.2022 5 Inspirient’sAutomated Analytics Engine Hybrid AI that autonomously applies analytical reasoning Structured / tabular data SQL / CSV / Excel / SPSS / JSON Analytical results Webapp to browse deduced insights
- 7. Copyright © 2022 by Inspirient GmbH. All rights reserved. Example #1 Basic AutomatedData Analysis 31.05.2022 6
- 8. Copyright © 2022 by Inspirient GmbH. All rights reserved. Four nodes identifiedas very central to network between 'Account ID' and 'IP address' Powered by Inspirient (www.inspirient.com) 10010 192.168.0.1 192.168.0.2 192.168.0.3 10011 192.168.1.1 192.168.1.2 192.168.1.3 192.168.1.4 192.168.1.5 10012 192.168.2.1 192.168.2.2 192.168.2.3 10013 192.168.3.1 192.168.3.2 192.168.3.3 10014 192.168.4.1 192.168.4.2 192.168.4.3 10015 192.168.5.1 192.168.5.2 192.168.5.3 10016 192.168.6.1 192.168.6.2 192.168.6.3 192.168.6.4 192.168.6.5 10017 192.168.7.1 192.168.7.2 192.168.7.3 10018 192.168.8.1 192.168.8.2 192.168.8.3 10019 192.168.9.1 192.168.9.2 192.168.9.3 Account ID IP address Most central node Note: A node is more central to the network if it is on the path between many other nodes (so-called betweenness centrality) Current Analytical Capabilities of AI Selection Focused on Anomaly Detection 7
- 9. Copyright © 2022 by Inspirient GmbH. All rights reserved. Agenda Fraud Detection withAI in Four Steps • Automated Data Analysis • AI-supported Anomaly Detection • The Human Factor • Real-time Fraud Detection 31.05.2022 8
- 10. Copyright © 2022 by Inspirient GmbH. All rights reserved. Difficulty in AdversarialDetection… Established AI approaches struggle with adversarial setting specific to fraud Established Approach #1 Rule-based Fraud Detection Traditional rule-based fraud detection results in cat-and- mouse game with fraudsters: Established Approach #2 ML-based Fraud Detection Current Machine Learning (ML) fraud detection suffers from model instability in adversarial conditions:1 9 To reliable detect fraud, we need to think beyond traditional AI silos – we need Hybrid AI “We published the rule on Friday – the fraudsters knew it by Monday!” —Head of Risk, Top 10 global bank 1. Source: “Why deep-learning AIs are so easy to fool” (Nature, Oct 2019) 31.05.2022
- 11. Copyright © 2022 by Inspirient GmbH. All rights reserved. …Calls for aHybrid Approach Blend of Machine Learning and Expert System combines strengths of both approaches 31.05.2022 10 Unsupervised learning + Effortless training without human resources − Requires significant training dataset (or ability to generate training data) Rule-supported supervised learning • Strikes balance between upfront human training and required training data • Can be trained incrementally, as part of regular business operations Supervised learning + Applicable if little training data is available − Results improve slowly after multiple iterations with human feedback
- 12. Copyright © 2022 by Inspirient GmbH. All rights reserved. Example #2 Real-world AnomalyDetection 31.05.2022 11
- 13. Copyright © 2022 by Inspirient GmbH. All rights reserved. Agenda Fraud Detection withAI in Four Steps • Automated Data Analysis • AI-supported Anomaly Detection • The Human Factor • Real-time Fraud Detection 31.05.2022 12
- 14. Copyright © 2022 by Inspirient GmbH. All rights reserved. The Actual Problem CognitiveBias: The Streetlight Effect 13 Streetlight Effect—Type of observational bias that occurs when people only search for something where it is easiest to look
- 15. Copyright © 2022 by Inspirient GmbH. All rights reserved. Knowledge Intuition KnowableFacts Patterns in data Evidence, Leads, … Hypotheses Reach of traditional analytics Added reach with Inspirient’s AI-supported analytics Working around Human Intuition How do we ask the right questions? 31.05.2022 14
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- 17. Copyright © 2022 by Inspirient GmbH. All rights reserved. Hands-on Collaborative Prioritization 31.05.202216 Example: Prioritization workshop at Deutsche Bahn Source: Bitkom Big Data Summit 2017
- 18. Copyright © 2022 by Inspirient GmbH. All rights reserved. Agenda Fraud Detection withAI in Four Steps • Automated Data Analysis • AI-supported Anomaly Detection • The Human Factor • Real-time Fraud Detection 31.05.2022 17
- 19. Copyright © 2022 by Inspirient GmbH. All rights reserved. Team / AISetup at BNP Paribas 31.05.2022 18 AI-supported Analytics Anti-fraud / risk team Feedback data Transaction data Client master data General- purpose statistical reasoning Fraud- specific reasoning Generated alerts Hybrid AI combines rule-based reasoning with supervised learning
- 20. Copyright © 2022 by Inspirient GmbH. All rights reserved. Deep dive: FeedbackLoop 31.05.2022 19 Determine target column Train classifiers Select best classifier Identify predictors 1. A target column can be specified by adding the PRIORITY annotation to the column label in the input table 2. Leave-one-out cross validation (LOOCV) is used for classifier selection Step #1: Target column(s) can be specified by user1 or will be chosen automatically by Inspirient Step #3: Inspirient automatically chooses appropriate predictive models for target variable and builds them from the predictor columns Step #4: The models built in step #3 are systematically tested through cross-validation2 to identify the best classifier Step #2: Predictor columns, i.e. dimensions used to build classification model, are automatically identified by Inspirient
- 21. Copyright © 2022 by Inspirient GmbH. All rights reserved. Deep-dive: Case Workflow Newphishing workflow automatically alerts risk team upon suspicious activity 20 User notification alert List of auto-detected suspicious activities for investigation 31.05.2022
- 22. Copyright © 2022 by Inspirient GmbH. All rights reserved. Sample Results (1/2) MoneyLaundering 21 29.08.2017 30.08.2017 28.08.2017 Transactions to [x] on 29 August 2017 stand out against typical daily distribution
- 23. Copyright © 2022 by Inspirient GmbH. All rights reserved. Sample Results (2/2) Phishing,Conspiracy, Wire Fraud 31.05.2022 22 Fraudsters will scout multiple accounts, and often overlap, allowing us to uncover a ‘fraudster network’
- 24. Copyright © 2022 by Inspirient GmbH. All rights reserved. Outlook: Quantifying Evidence Nextstep is to measure collected datasets to prioritize investigations 31.05.2022 23 Please get in touch if this use case is relevant to you / your team!
- 25. Copyright © 2022 by Inspirient GmbH. All rights reserved. Dr. Georg Wittenburg Phone:+49 30 2007 4820 Email: georg.wittenburg@inspirient.com Web: www.inspirient.com 24 31.05.2022
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- 27. Copyright © 2022 by Inspirient GmbH. All rights reserved. Inspirient’s Hybrid AI forFraud Detection and Prevention Selected milestones • 2016 – Established in Berlin by Dr. Georg Wittenburg (ex-BCG) and Dr. Guillaume Aimetti (ex-Deloitte) • 2016 – Minority investment from Prof. A.-W. Scheer • 2017 – Won group-level BNP Paribas Intl. Hackathon with hybrid fraud prevention solution1 • 2019 – Boosted expense appraisal accuracy to +95% at “Big 4” auditor at +70% cost reduction2 • 2021 – Launched analyst-2.ai for fully autonomous analysis of public data, incl. data quality assessment Inspirient’s Automated Analytics Engine • Award-winning Hybrid AI system that employs proba- bilistic reasoning and procedural generation to fully automate data analyses and pattern detection • Focus on communicating results to non-technical audience, e.g., as business presentation3 • World’s first AI-generated slide discussed in board meeting of German insurance company in 2018 26 1. Special commendation for scalability and integration (press release) 2. On 20 FTE out-sourced team, while also increasing decision transparency and closing process gaps 3. Profit optimization example at https://youtu.be/G1NR2LCQFtw 31.05.2022
- 28. Copyright © 2022 by Inspirient GmbH. All rights reserved. Hybrid AI “Inspirient isa leader in this trend” 27 “After years of Deep Learning (DL) hype, the AI community is now shifting towards hybrid DL + X approaches to achieve real-world applicability. In Germany, Inspirient is a leader in this trend.” —Prof. Dr. rer. nat. Dr. h.c. mult. Wolfgang Wahlster Founding Director and Chief Executive Advisor German Research Center for Artificial Intelligence (DFKI) 31.05.2022
- 29. Copyright © 2022 by Inspirient GmbH. All rights reserved. Won inaugural BARC StartupAward for analytics and data management Presentation available online Won group-level BNP Paribas Hackathon for intl. integration and scalability of our fraud prevention solution Press release available online Only German startup in Top 10 of NATO Allied Command Transformation (ACT) innovation chal- lenge on data analysis Press release available online Top 10 in Deloitte’s RegTech Universe for compliance work in cyber fraud and AML1 Full list available online 28 Inspirient’s Award-winning AI Winner BARC Startup Award 2016 Winner BNP Paribas Intl. Hackathon 2017 Top 10 NATO ACT Innovation Challenge 2018 Global Top 10 Deloitte RegTech Universe 2019 INNOVATION HUB 1. Anti-Money Laundering 31.05.2022
- 30. Copyright © 2022 by Inspirient GmbH. All rights reserved. • Setting –German branch of BNP Paribas was concerned about evolving identify theft, account hijacking and money laundering • Approach – Continuously monitor stream of financial transactions for abnormalities, while learning through feedback loop with risk team • Result – Highly accurate alerts to risks team, in time before money leaves the bank; uncovered unknown network of fraudsters Reference Case (1/4) Cyber Fraud Prevention in Banking 29 Client Impact – ~€12M operational cost reduction across five entities (FTEs / IT cost) Client financial transactions (sanitized) Fraud alert 31.05.2022
- 31. Copyright © 2022 by Inspirient GmbH. All rights reserved. • Setting –Top 3 German bank asked for data-driven review of their internal processes for handling business loans • Approach – Comprehensive pattern mining incl. AI-supported ranking across all dimensions of provided data sample • Result – In first iteration, identified previously unknown process gap for high-risk cases that led to ~8K mishandled business loans Reference Case (2/4) Process Mining: Finance / Risk 30 Client Impact – Reduced financial risk by quantifying critical process gap 31.05.2022
- 32. Copyright © 2022 by Inspirient GmbH. All rights reserved. • Setting –Anticipating regulation, client wanted to understand potential of chemical processing plants to reduce CO2 emissions • Approach – Full multi-year and multi- configuration plant telemetry was mined for process drivers of CO2-related KPIs • Result – Key driver analysis of target KPIs highlights underperforming process steps and sequences Reference Case (3/4) Anomaly Detection: Plant Optimization 31 Client Impact – Optimized plant procedures allow to reach target results with less CO2 31.05.2022
- 33. Copyright © 2022 by Inspirient GmbH. All rights reserved. • Setting –“Big 4” auditor wanted to reduce operational cost by automating outsourced 20-FTE expense appraisal process • Approach – Split process in sub-decisions and used client process documentation and historical data to train ~35 neural networks • Result – Automated system reached +95% decision accuracy, a ~20% improvement over outsourced human process Reference Case (4/4) Process Automation: Controlling 32 Client Impact – ~75% cost reduction, while increasing process accuracy Client process owners and users Client process documentation + − ≥ × ÷ ≤ Rule-based processing Machine Learning + − ≥ × ÷ ≤ + − ≥ × ÷ ≤ + − ≥ × ÷ ≤ Hybrid AI 31.05.2022
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