Graph Gurus Episode 34: Graph Databases are Changing the Fraud Detection and ...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-34
During this webinar we:
-Examine how graph analytics can lower the total cost of fraud;
-Describe how graph analytics can improve credit card fraud detection;
-Explore the application of graph analytics to an anti-money laundering use case.
A brief overview of the use of big data analytics in retail banking. This basic material is an introduction to the video training series: Retail Banking Analytics, available at briastrategy.com.
Graph Gurus Episode 34: Graph Databases are Changing the Fraud Detection and ...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-34
During this webinar we:
-Examine how graph analytics can lower the total cost of fraud;
-Describe how graph analytics can improve credit card fraud detection;
-Explore the application of graph analytics to an anti-money laundering use case.
A brief overview of the use of big data analytics in retail banking. This basic material is an introduction to the video training series: Retail Banking Analytics, available at briastrategy.com.
Using Big Data in Finance by Jonah EnglerJonah Engler
How can you utilize Big Data in the Financial Industry? To leverage Big Data - entrepreneur and finance expert Jonah Engler, has put together this presentation to help the slideshare community understand the value - and HOW TO - use big data in the financial campaigns.
Jonah Engler is a financial expert and stock broker based in NYC. Leveraging his experience in finance, Engler has gone on to have success in the franchise, coffee, startup industries and more. To connect with Jonah - checkout his profile on LinkedIn: https://www.linkedin.com/in/jonahengler
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Welcome to the Age of Big Data in Banking Andy Hirst
Big Data in banking presentation from Sibos Dubai 2013 . What are use cases driving deployments in Banking ? See the use cases SAP is involved In banking in 2013
BIG Data & Hadoop Applications in FinanceSkillspeed
Explore the applications of BIG Data & Hadoop in Finance via Skillspeed.
BIG Data & Hadoop in Finance is a key differentiator, especially in terms of generating greater investment insights. They are used by companies & professionals for risk assessment, fraud detection & forecasting trends in financial markets.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Check out how big data is proving invaluable to finance. Here is the top 10 big data trends in finance. Big data place a vital role in analysing the feeds, Predictive models, forecasts & assess trading impacts
Big data & analytics for banking new york lars hambergLars Hamberg
BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015.
Keynote address: "How Predictive Analytics will change the Financial Services Sector”
Speaker : Lars Hamberg
http://www.specialistspeakers.com/?p=8367
Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition
Creating $100 million from Big Data Analytics in BankingGuy Pearce
A sanitized version of our presentation to the Teradata Marketing Summit in Los Angeles in March 2014, on how we created $94.95 million in incremental value for a bank by means of a customer-centricity strategy enabled by Big Data and Analytics
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
Using Big Data in Finance by Jonah EnglerJonah Engler
How can you utilize Big Data in the Financial Industry? To leverage Big Data - entrepreneur and finance expert Jonah Engler, has put together this presentation to help the slideshare community understand the value - and HOW TO - use big data in the financial campaigns.
Jonah Engler is a financial expert and stock broker based in NYC. Leveraging his experience in finance, Engler has gone on to have success in the franchise, coffee, startup industries and more. To connect with Jonah - checkout his profile on LinkedIn: https://www.linkedin.com/in/jonahengler
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Welcome to the Age of Big Data in Banking Andy Hirst
Big Data in banking presentation from Sibos Dubai 2013 . What are use cases driving deployments in Banking ? See the use cases SAP is involved In banking in 2013
BIG Data & Hadoop Applications in FinanceSkillspeed
Explore the applications of BIG Data & Hadoop in Finance via Skillspeed.
BIG Data & Hadoop in Finance is a key differentiator, especially in terms of generating greater investment insights. They are used by companies & professionals for risk assessment, fraud detection & forecasting trends in financial markets.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Check out how big data is proving invaluable to finance. Here is the top 10 big data trends in finance. Big data place a vital role in analysing the feeds, Predictive models, forecasts & assess trading impacts
Big data & analytics for banking new york lars hambergLars Hamberg
BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015.
Keynote address: "How Predictive Analytics will change the Financial Services Sector”
Speaker : Lars Hamberg
http://www.specialistspeakers.com/?p=8367
Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition
Creating $100 million from Big Data Analytics in BankingGuy Pearce
A sanitized version of our presentation to the Teradata Marketing Summit in Los Angeles in March 2014, on how we created $94.95 million in incremental value for a bank by means of a customer-centricity strategy enabled by Big Data and Analytics
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
This is a presentation by Bizuneh Bekele, ePayment and FinTech Development Consultant, DigiFinance Africa, at the 3rd Annual East Africa Finance Summit
07 factors to consider while choosing an ecommerce payment gatewaySnehaDas60
As we all know, ecommerce portal conversion rates fall as a result of a lack of research when choosing a payment gateway.There are plenty of advance payment channels now that internet commerce has taken over the globe. Choosing the most potent ones, on the other hand, is essential to making the most of it.
For those who have a good understanding of payment gateways, let's look at the important elements to consider when selecting one for your eCommerce site.
Read more......
PSD2: The Advent of the New Payments Market in EuropeTransUnion
Register today for this webinar that summarizes Aite Group’s PSD2 Research Report, commissioned by iovation, a TransUnion Company, providing an in-depth analysis of how those in the payment services and e-commerce market should prepare to handle the new strong customer authentication (SCA) requirements under the second Payment Services Directive (PSD2).
Join Angie White, Product Marketing Manager and PSD2 expert at iovation, a TransUnion Company, and Ron Van Wezel, Senior Analyst at Aite Group's Retail Banking and Payments Practice, as they analyze the results of the actual market status in Europe regarding the main changes that PSD2 will bring to the online payments market. Learn what Aite Group concluded after interviewing 20 payments executives from European banks, other PSPs, merchants, payment networks and industry experts.
Key takeaways:
The impact of PSD2, highlighting the priorities that organizations have yet to manage in the transition to the new world after PSD2.
How organizations seek to implement the requirements for secure customer authentication (SCA) and minimize the impact on customer experience.
An analysis of the potential of payment innovation and open banking as a result of PSD2.
If you haven’t already, register for this complimentary research report, PSD2: Advent of the New Payments Market in Europe.
Read the overview of the implications of PSD2 for the payment space in relation to fraud prevention and authentication, including recommendations for banks and other players on how to comply while minimizing friction during the payment process.
The New Payments Platform: Fast-Forward to the FutureCognizant
Today's bank customers demand digital payment instruments that support real-time payments and settlements. While banks worldwide have adopted this concept, Australia's New Payments Platform (NPP), when contrasted with global models, takes this concept a step further with benefits that include all of the features today's bank customers want, such as 24x7x365 availability; real-time settlement, posting in seconds, premier messaging standards and alternate identifiers. It is thus imperative to build a carefully planned, all-inclusive NPP solution that will remain viable, profitable, efficient and serviceable from internal, regulatory, payments and customer perspectives alike.
Software for Payment Cards: Choosing WiselyCognizant
As the use of card-based payments continues to grow, financial institutions must improve their response times, strengthen their security, hone their future-readiness and enrich their business value. When selecting a commercial off-the-shelf (COTS) solution, banks must verify that the product and its support services are equipped to accommodate short and long-term business and IT objectives.
Payments Pulse Survey: Small Business EditionPayments Canada
Of the over one million employer businesses in Canada, 99.7 per cent represent small and medium-sized enterprises (SMEs), leaving 0.3 per cent representing large businesses. As a key economic driver, Payments Canada decided to focus a survey specifically on the payment interests of Canadian SMEs, Payments Pulse Survey: Small Business Edition.
Building on our E&Y report How can payments modernization benefit Canadian businesses? released earlier this year, we dug deeper to find out how payments systems meet SMEs’ business needs, how inefficiencies in current payments processing impact SMEs and how SMEs anticipate benefiting from a new payments system.
The introduction of new systems, rules and standards as part of Payments Canada’s Modernization program will foster a faster, safer and more data-rich payments environment. The top anticipated enhancements will come from new real-time payments, giving small businesses more choice in how they make their payments, and the adoption of the ISO 20022 data standard, which has the potential to improve automation and efficiency by increasing the data that travels with a payment.
What we heard loud and clear is that Canadian SMEs are ready for more payment options. They want more choice for their customers at point-of-sale and more options for their back-office payments to suppliers and vendors, such as e-transfers, e-wallets and the digital currencies. And of course, they want their payments to be safe and secure.
Finally, the survey found that overwhelmingly the majority of SMEs are willing to integrate new technologies into their operations to meet future payment needs.
Risk Beyond Acquiring: Merchant Risk Across FinTechGeo Coelho
In our Vendor Spotlight at MAC 2016, Jose Caldera, IdentityMind VP of Marketing & Product, presented a collection of use cases and examples of how IdentityMind clients are applying our platform for merchant risk, fraud prevention, anti-money laundering, and terrorist financing prevention.
The session provided a great introduction to the benefits of our platform, and we've provided a synopsis of it here for those who missed the session, or were curious for more information.
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Fraudulent credit card cash-out detection On Graphs
1. Perfecting IT service and favoring clients 'success
Fraudulent credit card cash-out detection
On Graphs
2. Fraudulent Credit Card Cash Out
The prevalence of Fraudulent credit card cashing out has led to a rise in the charge-off and delinquency rate
of credit cards, meanwhile the joint debt risk is transmitted to banks.
Bank 2014 2015 2016 2017 2018 2019
Construction
Bank
0.85% 1.08% 1.28% 1.17% 1.09% 1.21%
Bank of
Communication
1.68% 1.82% 2.14% 1.98% 1.84% 2.49%
China
Merchants Bank
0.94% 1.37% 1.21% 1.26% 1.14% 1.30%
China CITIC
Bank
- - 1.41% 1.30% 0.98% 1.74%
Ping An Bank 2.77% 2.5% 2.15% 1.20% 1.19% 1.37%
The charge-off and delinquency rate of major banks
in the past five years
3. Credit card fraud
Credit card fraud detection
Graph Searching
Rating Scale
Multipartite graph
Application Control
Customer Rating
Anti – Cash Out
Anti - Fraud
Transaction Early
Warning
Technology Application
Feature Engineering
Early stage
of Loan
Middle and Late
Stage of Loan
Machine Learning
Graph Embedding
Credit card fraud Scene
Skimming
Account takeover
Fake card
Application Fraud
Fraudulent Cash out
5. Data Problem of Bank
Massive Bank
Statements
Perfect Bills Data Island
Scarce Bad
Samples
6. Graph Schema
Building heterogeneous information network based on a large size of transaction records.
Vertic
es
• Clients
• Cards
• Business fields
Edges
• Relationship
7. Credit Card
Debit CardConsume
Payment
Store
The aim of detection is to find out the loop of
credit card funds inflows and outflows from
the graph.
Rules of fraudulent credit card cash-out detection
Revolving Credit: High frequency of transaction;
Consumer Credit: Installment credit, large transaction.
8. 1. Improve detection effect ;
--Fund Recycle Detection
2. Improve iteration efficiency;
--28 Times
3. Improve coverage efficiency
--77%.
Result Based on Rules Display
Business Interview,
Rules Combing
Sample Analysis,
Result comparison
Implement Rules
78 rules in total; 8 main cash-out methods
9. Graph-based transaction detection
We apply densest subgraph detection for cash-out transaction.
2.Credit card and debit card transfer
data
1.Detect fraudulent cash-out credit
card and store
3.Densest subgraph detection
11. Build Graph Model
Table: Notations and symbols
Symbols Interpretation
Tripartite graph of transfers in the bank
Set of nodes, indicating accounts
Set of edges, indicating transaction
Set of credit cards
Set of POS machine settlement account
Set of transaction account
Set of final debit card account where
fund flow trans to
Transaction between credit card and
merchant using POS machine
Transaction among different accounts
Credit card
purchase transaction
POS Machine Debit Card
13. Start from the whole graph, each time we can remove the node that has smallest contribution
to dense subgraph, in order to keep the node that can make density function approximates.
Build a priority tree , keep the weight of all its connected nodes and then choose the
minimum value of the sub node as the branch node.
Search dense subgraph
Greedy Approximation Search
Heuristic Optimization
Computational Efficiency
14. Result Disposal
Merchant closure rate 35%,
Disposal rate 75%.
Implement
Output top ten suspicion
subgraphs of GSQL
Result Based on Densest Subgraph Detection Display
Preparation
Credit card transaction data
and Settlement account
transfer data of all
merchants in the bank