Ai and machine learning help detect, predict and prevent fraud - IBM Watson Data Science Meetup
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Nina Lozo - Data & AI Technical Professional, IBM
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Ai and machine learning help detect, predict and prevent fraud - IBM Watson Data Science Meetup
- 1. AI and machine learning help detect, predict and prevent fraud Nina Lozo Data & AI Technical Professional IBM South East Europe, nina.lozo@rs.ibm.com IBM Watson Data Science Meetup
- 2. 72% of businesses cite fraud as a growing concern and 63% report the same or higher levels of fraudulent losses over the past 12 months – Experian Global Fraud Report Data Science and AI/ March, 2019 / © 2019 IBM Corporation 2
- 3. © 2019 IBM Corporation Fraud Detection improvement with Data Science
- 4. © 2019 IBM Corporation 4 Fraud Detection without data science
- 5. © 2019 IBM Corporation 5 Fraud Detection improvement with Data Science
- 6. © 2019 IBM Corporation 6 Fraud Detection improvement with Data Science
- 7. © 2019 IBM Corporation Enforcement funnels
- 8. Data Science in Fraud Detection 8 A data science platform is complimentary to other fraud detection systems • React: Predictive models can quickly determine changing patterns in fraud and react to them in real time • Improve: Data science can help derive new fraud detection rules, which can be used to improve the business process • Achieve more: Data science can increase the rate of fraud detection
- 9. 9Data Science and AI/ March, 2019 / © 2019 IBM Corporation Challenge #1: Digital transformation exacerbates data issues Data silos Multiple fraud departments Internal point-solutions Data overload Different channel, product or fraud type Incomplete view Unable to see patterns or behaviors across business lines
- 10. 10Data Science and AI/ March, 2019 / © 2019 IBM Corporation Need for an end-to-end data science platform Provide one consistent experience Across multiple departments Ready access from public clouds, private clouds, and on-premises Strong governance and security Collaborate across LOB and SMEs Across data scientists, risk analysts, investigators and other subject matter experts See a single view of latest insight Continuously accommodate real-time data, monitor and detect fraudulent activities Adapt as the patterns change and spot anomalies
- 11. 11Data Science and AI/ March, 2019 / © 2019 IBM Corporation Challenge # 2: Increasingly difficult to predict fraud Fraud is rare Can be as low as 1% of activities Imbalance in classification of detection models Faster turnaround Transactions executed more quickly Less time to identify, counteract, and recover Fast-evolving fraudster schemes Unceasing new and mutating forms New routes and channels
- 12. 12Data Science and AI/ March, 2019 / © 2019 IBM Corporation More data science for more people Visual programming tool for LOB experts and analysts (citizen data scientists) Increase efficiency of “known” threats Faster discovery and deployment Full end-to-end AI lifecycle: build, deploy, and manage More time to identify, counteract, and recover Deep learning and advanced analytics Enable deep learning and neural networks Get latest models and frameworks in fraud prediction Need to move from fraud detection to fraud prediction Need to upskill the team to do more data science
- 13. 13Data Science and AI/ March, 2019 / © 2019 IBM Corporation Challenge # 3: Fraud detection is costly False positives Require manual investigations Poor customer experience Loss payout $millions and resources reserved for payout Poor customer experience Reputation risk Potential damage to company reputation
- 14. 14Data Science and AI/ March, 2019 / © 2019 IBM Corporation Fewer false positives Leverage more unstructured data like text and images Enable deep learning and neural networks Faster model training and development Leverage pre-trained APIs to jump start development process Easy tooling to train models in a few clicks Need to make faster and more accurate predictions
- 15. Advanced analytics techniques can dramatically improve the effectiveness and efficiency of fraud management… Where once fraud was detected by risk functions flagging suspect transactions for manual review, firms can now use neural networks based on unsupervised and supervised architectures to monitor dubious activities. – McKinsey & Company Fraud management: Recovering value through next-generation solutions
- 16. 16 AI drives real business value in fraud prevention Faster screening updates Enhanced screening models Enhanced accuracy of fraud profiling Enhanced identity verification Centralization of fraud processes Enhanced fraud analytics tools Lower cost of fraud infrastructure Reduced fraud false positive rates Improved investigations process Facilitate investigation case management Automated fraud reporting Comply with voluntary and mandated regulations while differentiating competitive position Reduced costs of payment fraud losses Reduced costs of fraud screening & monitoring Reduced cost of fraud investigations Reduced cost of compliance reporting of fraud
- 17. AI enables you to predict likelihood of fraud and proactively act upon insight to drive better prevention Capture Data Collection delivers an accurate view of customer attitudes and opinions Predict Act Predictive capabilities bring repeatability to ongoing decision making, and drive confidence in your results and decisions Unique deployment technologies and methodologies maximize the impact of analytics in your operation … … Data Collection Deployment TechnologiesPlatform Deep Learning Detect Predict Analyze Data Mining Machine Learning
- 18. Easily & seamlessly move from sandbox to production Connect data science models with real-time data Deploy predictive models into business process Create more “citizen data scientists” with visual modeling Train advanced ML models without data science degree Make fraud detection easier Empower data scientists to get ahead of fraudsters Enable deep learning and neural networks Get latest models and frameworks in fraud prediction Leverage more unstructured data like text and images Easy tooling to train models in a few clicks Leverage pre-trained APIs to jump start development process Move from fraud detection to fraud prediction Upskill the team to do more data science Stay ahead of fraudsters with latest ML models Make faster and more accurate prediction Strategies to stay ahead
- 19. 19 “With the data mining system, we generated productivity savings of nearly 80 percent.” Francisco Ruiz Head of Compliance, Bancolombia Solution Deployed predictive data-modeling software that helped it more easily and quickly detect transactions that were part of potential money-laundering operations Solution prevents, detects and reports potentially fraudulent banking activities that may stem from criminals and terrorists Challenges Need to analyze millions of daily transactions to identify current and potential fraud Move from a labor-intensive decentralized system to a more automated process Results Reveals 40% more suspicious transactions by automatically identifying the most likely fraudulent activities. Increases reporting capabilities by 200% and analysts productivity by 80% Discovers the latest money-laundering techniques by capturing data from 700 branches and 2,300 ATMs in six countries. Aggregates multiple transaction activities with centralized reporting for more precision in detecting financial relationships.
- 20. 20 "The IBM Data Science Elite team was able to help direct our operating model, and skills, towards a deeper and more integrated structure." Guy Taylor Head of Data & Data-Driven Intelligence Solution Deployed predictive data-modeling software that helped it more easily and quickly detect transactions that were part of potential money-laundering operations Solution prevents, detects and reports potentially fraudulent banking activities that may stem from criminals and terrorists Challenges Fraudulent activity is very rare relative to all online banking activity, making it difficult to predict, posing a reputational risk to the bank Current fraudulent alert system has a very high false positive rate, lowering customer satisfaction Results Reduce number of alerts that fraud responders must review and reduce missed fraudulent activity Assist fraud responders in identifying which suspicious activities are most likely to be fraudulent.
- 21. 21 FIRST MODELING APPROACHCHALLENGES • Fraudulent activity is very rare relative to all online banking activity (0.004% of sessions) • ~500M actions/ month • Predictors need to be accepted by fraud team Nedbank: Predict Fraudulent Online Banking Activity SECOND MODELING APPROACH 21 OBJECTIVE • Use supervised machine learning to predict fraudulent activity within Nedbank's mobile banking system OVERVIEW • Currently uses a decision-rule based system to flag suspicious transactions for review by fraud responders • High false positive rate, low false negative rate • Missed fraudulent activity is costly • Large volume of alerts places a burden on responders 94% 48% CURRENT SYSTEM WITH AUGMENTATION False Positives 4% 7% CURRENT SYSTEM WITH AUGMENTATION False Negatives 95% 85% CURRENT SYSTEM ML MODEL False Positives 17% 6% CURRENT SYSTEM ML MODEL False Negatives • Augment existing system by predicting which alerts on individual activity are correct • Predict which user sessions are fraudulent within first 10 seconds
- 22. 22 “Before this solution, the minimum time it took to settle a claim was three days. Now, the low-risk claims that pass down the ‘immediate’ channel can be settled within an hour.” Anesh Govender Head of Finance, Reporting and Salvage at Santam Solution Santam chose IBM for the range of functionality, flexibility, and its ability to integrate with an existing system The company’s core claims management system resided on a mainframe platform that still met the company’s needs The solution integrated different kinds of rules from across the infrastructure, including process rules from company’s business process management software system, decision and agility rules from SPSS software itself, and override Challenges Fraud losses accounted for an annual 6 to 10 percent of premium costs for Santam customers Needed a solution that more effectively assessed risk and separated potentially fraudulent claims from lower risk ones would prevent fraud, reduce other costs and increase efficiency Results Identified a major fraud ring in less than 30 days after implementation. Saved more than USD2.5 million in payouts to fraudulent customers, and nearly USD5 million in total repudiations. Reduced claims processing time on low-risk claims by nearly 90 percent.
- 23. 23 “The [Watson] Studio gives us the ability to process millions and millions of records and to be able to act real time.” Julio Sánchez Global Analytics Lead - Accenture Center for IBM Technologies, Accenture Solution By analyzing internal company and external data to determine the risk factors and level associated with each service user and alerts audit managers of risky behavior Challenges Detects the likelihood of fraudulent behavior – such as an individual posing as a legitimate customer who receives a service but won’t pay for it The ability to identify and trace new anomalous behaviors through continuous monitoring is vital for companies to take preemptive actions against future costly occurrences of fraud Results Process millions of records and be able to act real time
- 24. 24 Reduces payments on fraudulent claims and improves its ability to collect payments from other insurance companies Solution IPCC implemented solution to rapidly identify and investigate suspicious claims and to expedite handling of unsuspicious claims in order to improve customer satisfaction. Challenges IPCC needed ways to automate the workflows and data gathering related to fraudulent and subrogated automobile claims. Results Accelerated payments collection Reduced costs of claims payments Yielded annual return on investment (ROI) of 403% for direct and indirect benefits and a payback within 3 months
- 25. Data Science and AI/ March, 2019 / © 2019 IBM Corporation 25 Single platform to train and deploy models to support both fraud investigation and prediction Easy-to-use visual modeling capabilities to enable “citizen data scientists” to work with advanced ML Same best-in-class platform to support deep learning frameworks and models The only platform to provide easy tooling to enable unstructured data for investigation and prediction Why IBM? Move from fraud detection to fraud prediction Upskill the team to do more data science Stay ahead of fraudsters with latest ML models Make faster and more accurate prediction
- 26. Where do we go from here? An IBM-led AI Journey Workshop provides the strategy and expertise to transform your business into a cognitive enterprise and unlocks the full potential of your data with AI. Briefing & Vision AI Journey Workshop Design & Validate Implement & Deliver Conclude & Expand Identify your unique business challenges and needs. Explore how AI is transforming every industry. Partake in an IBM-led half or full day workshop to explore your use case and scope out potential solutions. Work with IBM subject matter experts to fully define the scope and success criteria for an AI solution. Delivery and deployment of the agreed upon AI solution, tailored specifically to your business needs. Explore how to further accelerate your organization’s AI Journey with IBM.
- 27. 27Data Science and AI/ March, 2019 / © 2019 IBM Corporation
- 28. © 2019 IBM Corporation Fraud Prediction in an Auto Insurance Claims Triaging System 28 The auto insurance claims triaging story – The target we’re driving towards: the claims triaging app – How to predict auto insurance claims fraud – Choosing a path through the Watson Studio demo that’s tailored for the customer’s users Building the fraud prediction model – Common tooling • Shopping for data using the catalog • Preparing data using Data Refinery – Canvas tool • Preparing training data • Training model – Notebook tool • Preparing training data • Training model – Watson Machine Learning • Deploying model
- 29. © 2019 IBM Corporation Challenge Processing insurance claims is expensive, time consuming, and risk-intensive. The most significant risk factors are litigation, which increases in likelihood the longer it takes to adjust a claim, and fraud. Claims processing become especially intense during natural calamities, when insurers need to process a sudden spike in claims, even to the point of transporting adjusters to the impacted location. Goals Using information that's available to the insurance company, develop a data-driven claims process that does the following: • Reduces the median time for a claim to be processed • Minimize the risk of fraud Method Using IBM’s self-service AI tools, develop an intelligent claims processing app 29 Fraud Prediction in an Auto Insurance Claims Triaging System
- 30. © 2019 IBM Corporation Claims Triaging App This app presents relevant data to claims triaging staff Focal point is the fraud prediction –Probability assigned –Reasons presented Watson Studio provides tools to build the data assets used by this app: refine data and build, train, deploy the fraud probability model 30
- 31. © 2019 IBM Corporation Fraud indicators Loss event claimed within 15 days of policy expiration Expired drivers license Expensive vehicle damages Frequent changes of residence High mileage at loss event for a policyholder with a low mileage discount High number of previous claims No police report Building a fraud prediction model 1. Find the data that shows the fraud indicators 2. Prepare a training data set with the fraud indicators 3. Train a fraud prediction model 4. Deploy the model for use in the fraud triaging app 31 Fraud Prediction in an Auto Insurance Claims Triaging System
- 32. © 2019 IBM Corporation Watson Studio has Tools for Multiple User Types 32
- 33. © 2019 IBM Corporation Introducing Watson Studio Collaboration environment for data-driven discovery Two basic concepts Catalog Records of your organization’s data assets Where you find data assets Projects Temporary sandboxes for discovery and development of new data assets Where you build data assets 33
- 34. © 2019 IBM Corporation Finding Data in the Catalog: Shop for Data Metadata-based search Explore data assets Social engagement: tagging, rating, commenting Find, and add data sets to your project Policy-based access control 34
- 35. © 2019 IBM Corporation Projects Sandbox environment Gather data assets Build data products with the data assets Collaboration Tools for different user types Easy for people to see each others’ work and pitch in 35
- 36. © 2019 IBM Corporation Canvas Tool GUI-based data preparation Flow-based operations Step-by-step we manipulate the data set until we have the fields needed for training Many operations available Operationalize Can push data preparation down to database engines Save the set of steps as a single data flow Writable to a staging destination Staged prepared data can be consumed by other tools 36
- 37. © 2019 IBM Corporation Notebooks Open tool from asset or “Add to Project” menu Programmatic environment Open source tooling, wrapped in a managed, secure collaboration environment 37
- 38. © 2019 IBM Corporation Model deployment: Watson Machine Learning Common tooling for deployed ML models Same tooling, regardless of model build approach Version control over models and their deployments Deployed models admin can be done through a REST API Scoring end point is also a REST API 38
Hinweis der Redaktion
- Using AI/ML for fraud detection is not new. However, typical organization contains multiple fraud departments, each with its own internal point-solution which monitors fraud for that specific channel, product, or fraud type. Structured and unstructured data collected internally and externally but very few of these point-solutions share data. Each uses varying analytical techniques across channels and transaction systems, which results in not having a complete view of risk exposures across the institution. Cannot see patterns or behaviors that would spark a concern that fraudulent activity is crossing multi-business lines because the observation space is too narrow.
- Combatting fraud and performing investigative action demands an end-to-end data science platform. It empowers an organization to scale analysis with ready access to public clouds, private clouds and on-premises. The platform also speeds modeling, training and deployment time and simplifies collaboration with data scientists, risk analysts, investigators, and other subject matter experts while adhering to strong governance and security posture. Further, in order to respond to new types of fraud, waste and abuse while minimizing false negatives and accelerating response, the platform needs to continuously accommodate real-time data, monitor and detect fraudulent activities and adapt as the patterns change and spot anomalies.
- Rare occurrences create an imbalance in the classification of fraud detection models and makes detection challenging. Shift to increased digital and mobile customer platforms led to transactions being executed more quickly, leaving banks and processors with less time to identify, counteract, and recover the underlying funds. As quickly as new technology is used to identify fraudsters, they themselves are identifying new ways of defrauding the bank. For instance, identity theft is mutating from card skimming to account takeovers (ATO). Synthetic identify, a scenario where fraudsters combine fragments of stolen or fake information to create a new identity and apply for financial products.
- Upskill team to do more data science: Single platform for all model development, regardless of expertise(open source coding frameworks for data scientists + visual programming tools for LOB experts and business analysts) Align business and technical teams to work rapidly with routine against new threats Bring together models developed from different departments and provide a 360 view of patterns and behaviors Increase efficiency of “known” threats while continuously improving models for new threats Faster discovery and deployment: Support the full end to end AI lifecycle by seamlessly integrating with data management to understand your data and know where it is, deployment to get to production faster, and to protect against bias and promote trust via traceability Fraud detection is complex and everchanging. - To get ahead, you need deep learning – accelerated GPU (train faster), integration w/ most popular framework for deep learning (getting latest models in deep learning to stay ahead of the fraudsters), visual tools (visually build complex neural networks to train more advanced modeling) – for the data scientists
- Organizations need to identify anomalies accurately and efficiently at the level of accounts, merchants, cardholders and locations. False positives require manual investigations through providing content analytics across primary internal and external data sources Fraud detection – meaning detecting fraudulent behavior after it occurs – forcing companies to set aside money and resources for the inevitable losses they will incur, costing financial institutions millions of dollars and destroying the customer experience. Financial institutions need to get in front of the problem and focus on fraud prevention.
- Advanced analytics techniques can dramatically improve the effectiveness and efficiency of fraud management. The integration of high-quality data sources (such as digital communications, geospatial data, and satellite imagery), the use of more sophisticated modeling techniques (such as machine learning, deep learning, and natural-language processing), and the introduction of automation technologies (such as natural-language generation and cognitive-computing algorithms) are transforming the way companies approach risk management.
- FASTER SCREENING UPDATES Higher detection of connected frauds thru faster updates to lists/models from monitoring analytics ENHANCED SCREENING MODELS: Higher fraud detection thru enhanced fraud screening models ENHANCED ACCURACY of FRAUD PROFILING Higher fraud detection thru enhanced scoring in fraud screening ENHANCED IDENTITY VERIFICATION Higher fraud detection thru automated visual identity verification processes CENTRALIZATION OF FRAUD PROCESSES Standardize, consolidate & automate fraud modeling across enterprise ENHANCED FRAUD ANALYTICS TOOLS Improve productivity of fraud analysts with model building & testing acceleration tools LOWER COST FRAUD INFRASTRUCTURE: Reduce cost of fraud analytics systems costs through use Big Data technologies REDUCED FRAUD FALSE POSITIVE RATES: Reduce number of fraud investigations through enhanced false positive rates IMPROVED INVESTIGATIONS PROCESS: Improve productivity of fraud investigators thru providing content analytics across primary internal and external data sources FACILITATE INVESTIGATION CASE MANAGEMENT Improve multi-person fraud investigations through use of case management and collaboration tools AUTOMATED FRAUD REPORTING: Reduce legal costs of fraud cases thru enhanced fraud discovery documentation
- The process Use predictive analytics to help predict the likelihood of fraud Use data mining for clustering, classification, and segmenting data to find patterns and associations related to fraud Use machine learning to detect anomalies in transactions and predict whether transactions are fraudulent Use text/web mining to analyze unstructured data for sentiment analysis, or variable extraction to flag fraudulent activity
- Connects insights on why fraud happens from deploy into production to predict to prevent it happening Data science make fraud detection faster. Enabling your non-data scientists (analyst, SMEs) to train advanced ML models without DS degree – through visual modeling. Fraud detection is complex and everchanging. To get ahead of fraudsters, you need deep learning – accelerated GPU (train faster), integration w/ most popular framework for deep learning (getting latest models in deep learning to stay ahead of the fraudsters), visual tools (visually build complex neural networks to train more advanced modeling) – for the data scientists In the case of unstructured data (text and image), IBM is the only vendor that can provide easy tooling (train models in a few clicks, image segmentation, sentiment analysis) – pre-trained APIs, easy-to-use visual tools
- Link to Reference Profile: http://w3-01.ibm.com/sales/ssi/cgi-bin/ssialias?infotype=CR&subtype=NA&htmlfid=0GLOS-87TQH2&appname=crmd#attachments AML Compliance Process >1.3 Million transactions / day Predictive modeling allows the bank to narrow down the number of transactions requiring detailed analysis by 95 percent, saving resources and speeding report production. Reduced the number of customers analyzed in each segment from 4,000 to 130, allowing for more targeted and cost-effective analysis Solution synopsis A bank in Colombia wants to adhere to stricter governmental regulations regarding the reporting of potentially fraudulent transactions by deploying IBM SPSS Modeler to centralize and automate its analysis of 1.3 million transactions per day; the new system can identify potential fraud more easily and more quickly, it can focus more precisely on between 5,000 and 6,000 transactions Special handling instructions The client has agreed to be a reference for sales situations. The status of any installation or implementation can change, so you should always contact the Primary Contact or Additional Contact named in the reference prior to discussing it with your client. Any public use, such as in marketing materials, on WWW sites, in press articles, etc., requires specific approval from the client. It is the responsibility of the person or any organization planning to use this reference to make sure that this is done. The IBM representative will, as appropriate, contact the client for review. You should not contact the client directly. Business need Bancolombia, a private bank based in Medellin, Colombia, serves 6 million customers in six countries. It needs to adhere to stricter governmental reporting requirements instituted in 2008, and to analyze millions of daily transactions to identify current and potential fraud. With its decentralized system, staff has to routinely analyze 120,000 customers and transactions per week. The bank wants to evolve from that labor-intensive decentralized system based on strict rules and parameters to a more automated one that would better detect unusual patterns or behavior. Solution implementation Bancolombia deployed IBM SPSS Modeler to improve its ability to identify potential money-laundering and other fraudulent activities. It increased the speed and precision of its compliance reporting, integrated and centralized data from its multiple branches and its lines of business, and substantially lowered the cost of analyzing individual transactions. It can now identify transactional activities that may have been distributed among multiple entities in order to circumvent statutory limits and currency regulations. Benefits of the solution By using IBM SPSS Modeler, Bancolombia was able to reduce the number of transactions it analyzed from 120,000 per week to between 5,000 and 6,000. By reducing the number of transactions it had to analyze, it was able to generate productivity savings of up to 80 percent. The increased efficiency it gained from the use of IBM SPSS Modeler also enabled it to increase the number of “suspicious operation” reports it files with the government from 400 to 1,200. At the same time, it has been able to submit reports of higher quality, which gives the government more information to pursue potential fraud. Previously, only 57 percent of the bank’s reports received the highest ratings in terms of quality and thoroughness. With the new system, 97 percent of the reports receive the highest rating. What Makes it Smarter: - Intelligent: Reveals 40 percent more suspicious transactions by automatically identifying the most like fraudulent activities. Increases reporting capabilities by 200 percent and analysis productivity by 80 percent. - Instrumented: Discovers the latest money-laundering techniques by capturing account data from 700 branches and 2,300 ATMs in six countries. - Interconnnected: Aggregates multiple transaction activities with centralized reporting for more precision in detecting financial relationships. Additional Smarter Planet information: - Intelligent: Reveals 40 percent more suspicious transactions by automatically mining 1.3 million transactions per day and identifying the most transactions most likely to be fraudulent. Previously, bank employees manually analyzed 120,000 customers and transactions per week. Because the new system can identify potential fraud more easily and more quickly, it can focus more precisely on a smaller number of transactions—between 5,000 and 6,000. Additional efficiency gains can be found in how the automated solution generated productivity savings of nearly 80 percent by reducing the number of staff needed to review massive transaction volume, while increasing reporting by 200 percent. - Instrumented: Discovers the latest money-laundering techniques and increases accuracy by capturing data from both commercial and personal accounts, and from 700 branches and 2,300 ATMs in six countries. The bank uses two specific identifiers crucial to understanding deviations: expected transactional patterns for different commercial segments, and normal transaction patterns for individual customers within each segment. By defining expected and typical patterns, the bank can then mine the data to identify either unusual transactions or sudden changes in behavior. - Interconnected: Aggregates activities that may be distributed among multiple entities in order to circumvent statutory limits and currency regulations by centralizing reporting from the bank’s 700 branches. This allows the bank to more precisely detect relationships between those who deposit money and those who receive money.Solutions/Offerings
- Special handling instructions The client has agreed to be a reference for sales situations. The status of any installation or implementation can change, so you should always contact the Primary Contact or Additional Contact named in the reference prior to discussing it with your client.
- Link to Reference Profile: http://w3-01.ibm.com/sales/ssi/cgi-bin/ssialias?infotype=RF&subtype=CS&htmlfid=SANS-985HX2&appname=crmd Public Case Study: http://www-03.ibm.com/software/businesscasestudies?synkey=P366760E07052S87 Client Name: Santam Insurance About the client - Santam is South Africa’s largest short-term insurance company with assets of over R17 billion (US$ 1.88 billion). It provides personal, commercial, agricultural, and specialist insurance policies throughout South Africa and holds additional businesses in Zimbabwe, Malawi, Uganda, Tanzania and Zambia Business Need: Santam faced the challenge of operating in an environment where fraud was estimated to account for between 6 and 10 percent of all premium revenue because of the challenges of managing complex claims while maintaining a high level of customer service. To solve this problem, Santam sought to find a more personalized method for managing each claim and prioritizing the effort needed to successfully investigate and mediate each claim. Solution Summary: The Head of Finance, Reporting and Salvage at Santam, sought to automate and manage these claims through a predictive analytics solution. Santam’s vendor evaluation process led to a decision to select either SAS Institute or Olrac SPSolutions (an IBM Partner), which was offering an IBM SPSS-based solution. Santam sought to create an advanced predictive analytics deployment and were sure that Olrac SPSolutions had the skills and prior experience necessary to build the necessary functionality from an IBM SPSS base solution. Results: Saved R17.9 million (US$ 1.98 million) in the first four months of use Benefits: The detection of an insurance fraud syndicate that had previously gone undetected; The use of predictive analytics to categorize claims also reduced the time and cost associated with settling cases; Low risk cases no longer have to go through the exhaustive due diligence that previously took at least three days to perform. Now, approximately 50 percent of these claims are accelerated through improved categorization. Fifteen percent of claims, or about 54,000 claims, can be processed in less than an hour, representing a 95 percent savings in time
- https://www.ibm.com/case-studies/Accenture Fraud detection in the telecommunications space is a major focus area for Accenture’s business.
- Infinity Property & Casualty Corporation (IPCC) is a provider of personal automobile insurance with an emphasis on nonstandard auto insurance. Nonstandard auto insurance provides coverage to drivers who, because of their driving record, age, or vehicle type, represent higher than normal risks and pay higher rates for coverage. The company’s products provide insurance coverage for liability to others for bodily injury and property damage, and for physical damage to an insured’s vehicle from collision and various other damages. IPCC distributes its products primarily through the Web and a network of independent agencies.
- Connects insights on why fraud happens from deploy into production to predict to prevent it happening Data science make fraud detection faster. Enabling your non-data scientists (analyst, SMEs) to train advanced ML models without DS degree – through visual modeling. Fraud detection is complex and everchanging. To get ahead of fraud, you need deep learning – accelerated GPU (train faster), integration w/ most popular framework for deep learning (getting latest models in deep learning to stay ahead of the fraudsters), visual tools (visually build complex neural networks to train more advanced modeling) – for the data scientists In the case of unstructured data (text and image), IBM is the only vendor that can provide easy tooling (train models in a few clicks, image segmentation, sentiment analysis) – pre-trained APIs, easy-to-use visual tools
- An IBM-led AI Journey Workshop provides the strategy and expertise to transform your business into a cognitive enterprise and unlocks the full potential of your data with AI. AI Journey Workshops are complimentary, from IBM to you. • Develop an actionable use case and roadmap to the future. • Design a high-level solution in support of the given use case. • Identify gaps and plan to address through detailed design.
- Once the backdrop of the auto insurance claims triaging story has been established, a best practice for this demo is to show the end result of what’s being built so your audience knows what they’re heading toward. In this case, what we’re using Watson Studio for is to explore the available data from the insurance company’s system of record to pull out data of interest for the claims adjusters, and to build, train, and deploy a claims fraud probability model. We’re also taking the location data and using it to get weather data for the date/time of the loss event, and plotting the locations of interest onto a map.
- Watson Studio features self-service tools designed for many different kinds of knowledge workers, ranging from business analysts who look for GUI tools, with MS Excel-like functionality, and data scientists who need tools to make the development and training of neural nets easier. There are two important points here: - teams can mix and match tooling (ie. Use refinery to prepare a data set, and train a model with this prepared data set in a notebook or canvas tool - all of these tools share a common environment, with governance tools, security, and management interfaces. No longer do people need to throw work over the fence by a team with specialized skills; now, these skilled users are in the same platform, where it’s easy for people to share results and work together.