Risk Assessment and Monitoring

Deep Link Risk Assessment and Monitoring in Real-time with TigerGraph

Business Challenge
Traditional Risk Management
Credit Risk
Regulatory Risk
Business Challenge
Risk assessment and monitoring has become more challenging with the rapid growth in size and complexity of the interconnected global financial markets. China, India and the rest of Asia have hundreds of millions of consumers joining the middle class every year and much of this population has limited financial transaction history and are not tracked by any credit bureaus. In the United States, 26 Million consumers are not tracked by FICO and related credit bureaus and that number has grown to 15% of the entire population as of 2018.
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This presents a unique challenge for credit risk assessment and monitoring. The regulatory landscape gets more complex each year with the fines for the United States Office of Foreign Assets and Control (OFAC) sanctions ranging from $12,500 to over $8.9 Billion for each sanctioned bank. The regulatory risk assessment and monitoring rises in importance as a result. The proliferation of complex financial instruments, such as credit default swaps and mortgage- backed securities have increased the complexity of liquidity risk assessment and monitoring and the cost of getting it wrong is in Trillions - The great recession of 2008 due to the mismanaged liquidity risk around subprime mortgages resulted in a staggering loss of 2 Trillion USD to the global growth and threatened the stability of global markets.
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Traditional Solutions Are Missing the Mark
Virtually all existing risk assessment and monitoring systems are built upon relational databases, which store information such as counterparty, account, transaction, stakeholders, financial instruments and derivatives in separate tables, one for each type of business entity. The relational databases are great tools for indexing and searching for data, as well as for supporting transactions and performing basic statistical analysis; however, the relational databases are poorly-equipped to connect across the tables or business entities and identify hidden relationships and risks from those relationships going across as many as 10 or more layers of transactions, accounts, and persons.
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Using the relational databases, in order to find potential connections, analysts need to join a number of large tables to run the queries. Such queries could take hours or even days to run, rendering any meaningful analysis of linkages among parties and transactions practically impossible. Assessing and monitoring risk also requires going beyond the internal data for the individual account or person and connect it up with the information from third party sources such as OpenCorporates and the World-Check database from Thomson Reuters containing information on Political Exposed Persons (PEP) and the government-sanctioned entities. In case of credit risk assessment, this means integrating non- traditional data sources such as mobile wallet and eCommerce transactions as well as microloan repayments. The relational databases with their rigid schema are not well suited for marrying the internal data with multiple external data sources with ease.
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Why TigerGraph - a Native Parallel Graph Database for Risk Assessment and Monitoring?
Credit Risk Assessment and Monitoring
As much as 15% of the US population isn’t tracked by FICO and associated credit bureaus. Fintech startup IceKredit is creating credit scores for these “credit invisible”, the subprime population in the United States as well as in China and Southeast Asia by tapping into alternate sources of credit history. Consider the example of IceKredit where a 360-degree view of the credit risk computed using TigerGraph leveraging any financial history using traditional data sources and combining it with the new data sources such as transactions on eCommerce sites such as WeChat and JD.com for China, mobile wallet records such as Alipay, Venmo and Paypal and the loan repayment records for Micro-loan and Peer-to-peer lending service providers such as CashBUS and Crowdo in Asia.
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With access to accurate third-party data (from the government, public and private sources), IceKredit uses Machine Learning and AI for custom advanced models and analytics used to build comprehensive credit views for applicants. IceKredit’s anti-fraud engines further quantify an applicant’s fraud probability and compare it with actual business activity, helping clients prioritize leads and make smarter decisions on acquiring customers based on risk. Graph analytics is key to IceKredit’s success in identifying undisclosed relationships and connections within data, and to assigning and updating risk ratings in real time. Graph analytics is also being used by IceKredit to assist and improve efficiency for the investigations around potential financial crimes violations.
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Regulatory Risk Assessment and Monitoring
Consider the example, where a bank receives an application for a new corporate account for a company ABC. During the customer due diligence or CDD process, the company fills out information including their street address as well as three officers for the company - Jim Smith, Wei Zhang and Maria Garcia. TigerGraph connects the company ABC officer Wei Zhang as a signatory for another company XYZ which has a local politician listed as a significant stakeholder or owner.
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From this information, TigerGraph connects the local politician with a third party database containing politically exposed persons (PEP) and also finds that one of the street addresses listed for the politician is same as the address listed for the new company ABC on their account application. All of this raises the risk associated with company ABC, raising an immediate CDD alert to be looked at and investigated further by the banking staff.
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Getting started with TigerGraph