Integrated risk assessment and management system

Abstract

Embodiments of the present invention relate to systems, apparatus, methods and computer program products for integrated risk assessment and management. More specifically, embodiments of the present invention provide for a risk database that collects and/or receives transaction data. In other embodiments of the present invention, the risk database collects and/or receives asset data and liability data associated with multiple financial institutions. The data is accessed to monitor customers' risk and one or more risk management actions are initiated based on the monitored customer risk.

What is claimed is: 
     
         1 . An apparatus for risk assessment and management, the apparatus comprising:
 a computing platform including at least one processor and a memory;   a risk database stored in the memory and configured to receive directly, from a plurality financial institutions, on an ongoing basis, customer transaction data and customer account data associated with customers of the financial institutions, and counterparty transaction data, wherein the counterparty transaction data is associated with counterparties that are entities with whom the customers transact; and   a risk evaluation module stored in the memory, executable by the processor and configured to monitor the data in the risk database to assess risk associated with customers, segments of customers and the counterparties and initiate one or more risk management actions based on the risk associated with the customers, the segments of customers and the counterparties, wherein the risk evaluation module includes,
 a behavioral baseline scoring routine configured to (1) determine behavioral baseline scores for (i) the customers, (ii) the segments of customers and (iii) the counterparties based at least in part on the customer transaction data, customer account data and the counterparty transaction data, including frequency of transactions, frequency of use of financial institution channels, transaction amounts and account balances, wherein each behavioral baseline score is customer-specific, segment of customers-specific or counterparty-specific and indicates normal risk in terms of financial transaction behavior for the customer, the segment of customers, and the counterparty, and (2) determining, through monitoring of the customer transaction data and the counterparty transaction data, a behavioral baseline score deviation, wherein the behavioral baseline score deviation indicates either a negative deviation of the behavioral baseline score indicating potentially risk inducing behavior or a positive deviation of the behavioral baseline score indicating potentially risk reducing behavior, and 
 a risk-alert routine configured to generate and initiate communication of an alert associated with the behavioral baseline score deviation. 
   
     
     
         2 . The apparatus of  claim 1 , wherein the risk database is further configured to receive, from the plurality of financial institutions, at least one of product data, account data, channel data or customer data. 
     
     
         3 . The apparatus of  claim 1 , wherein the risk database is further configured to receive, from the plurality of financial institutions, at least one of asset data or liability data. 
     
     
         4 . The apparatus of  claim 1 , wherein the risk database is further configured to receive, from the plurality of financial institutions, negative data. 
     
     
         5 . The apparatus of  claim 1 , wherein the risk database is further configured to receive, from the plurality of financial institutions, claims data. 
     
     
         6 . The apparatus of  claim 1 , wherein the risk database is further configured to receive, from one or more non-financial institution entities, non-financial institution data. 
     
     
         7 . The apparatus of  claim 6 , wherein the risk database is further configured to receive the non-financial institution data, wherein the non-financial institution data includes one or more of transaction data, product data, account data, channel data, customer data, negative data, counterparty data, asset and liability data or claims data. 
     
     
         8 . The apparatus of  claim 1 , wherein the risk evaluation module further comprises a risk pattern analysis routine configured to identify an emerging risk type based on at least the customer transaction data or the counterparty transaction data. 
     
     
         9 . The apparatus of  claim 8 , wherein the risk evaluation module further comprises a risk scoring module configured to determine a risk score for the customers, the segments of customers and the counterparties, wherein the risk score is associated with one or more risk types and is based on risk patterns and the customer transaction data or counterparty transaction data, wherein the risk score indicates a likelihood of a negative financial impact when doing business with an associated customer, segment of customers or counterparty based on one or more specific types of risk. 
     
     
         10 . The apparatus of  claim 1 , wherein the risk evaluation module further comprises an identity monitoring routine configured to monitor the customer transaction data for suspicious activity potentially associated with an identity misappropriation incident. 
     
     
         11 . A method for risk assessment and management, the method comprising:
 receiving directly from a plurality of financial institutions, on an ongoing basis, at a risk database stored in computing device memory, customer transaction data and customer account data associated with customers of the financial institutions, and counterparty transaction data associated with counterparties that are entities with whom the customers transact;   monitoring, by a computing device processor, the customer transaction data, customer account data and the counterparty transaction data to assess risk associated with the customers, segments of customers and the counterparties by (1) determining behavioral baseline scores for (i) the customers, (ii) the segments of customers, and (iii) the counterparties based at least in part on the customer transaction data, customer account data and the counterparty transaction data, including frequency of transactions, frequency of use of financial institution channels, transaction amounts and account balances, wherein each of the behavioral baseline scores is customer-specific, segment of customers-specific or counterparty-specific and indicates normal risk in terms of financial transaction behavior for the customer, the segment of customers and the counterparty, and (2) determining a behavioral baseline score deviation, wherein the behavioral baseline score deviation indicates either a negative deviation of the behavioral baseline score indicating potentially risk inducing behavior or a positive deviation of the behavioral baseline score indicating potentially risk reducing behavior; and   initiating, by a computing device processor, one or more risk management actions based on the risk associated with the customers including generating and initiating communication of an alert associated with the behavioral baseline score deviation.   
     
     
         12 . The method of  claim 11 , further comprising receiving, at the risk database stored in the computing device memory, at least one of product data, account data, channel data or customer data associated with the plurality of financial institutions and wherein monitoring further comprises monitoring, by the computing device processor, the at least one of product data, account data, channel data or customer data to assess the risk. 
     
     
         13 . The method of  claim 11 , further comprising receiving, at the risk database stored in the computing device memory, at least one of asset data or liability data associated with the plurality of financial institutions and wherein monitoring further comprises monitoring, by the computing device processor, the at least one of the asset data or the liability data to assess the risk. 
     
     
         14 . The method of  claim 11 , further comprising receiving, at the risk database stored in the computing device memory, negative data associated with the plurality of financial institutions and wherein monitoring further comprises monitoring, by the computing device processor, the negative data to assess the risk. 
     
     
         15 . The method of  claim 11 , further comprising receiving, at the risk database stored in the computing device memory, claims data associated with the plurality of financial institutions and wherein monitoring further comprises monitoring, by the computing device processor, the claims data to monitor the customer's risk. 
     
     
         16 . The method of  claim 11 , further comprising receiving, at the risk database stored in the computing device memory, non-financial institution data associated with one or more non-financial institution entities. 
     
     
         17 . The method of  claim 16 , wherein receiving the non-financial institution data further comprises receiving, at the risk database stored in the computing device memory, non-financial institution data, wherein the non-financial institution data includes one or more of transaction data, product data, account data, channel data, customer data, negative data, counterparty data, asset and liability data, or claims data. 
     
     
         18 . The method of  claim 11 , further comprising identifying, by a computing device processor, an emerging risk type based at least in part on the customer transaction data or the counterparty transaction data. 
     
     
         19 . The method of  claim 18 , further comprising determining, by a computing device processor, a risk score for the customers, the segments of customers and the counterparties, wherein the risk score is associated with one or more risk types and is based on risk patterns and the customer transaction data or counterparty transaction data, wherein the risk score indicates a likelihood of a negative financial impact when doing business with an associated customer, segment of customers or counterparty based on one or more specific types of risk. 
     
     
         20 . The method of  claim 11 , wherein monitoring further comprises monitoring, by the computing device processor, at least the customer transaction data to identify suspicious activities potentially associated with identity misappropriation incidents. 
     
     
         21 . A non-transitory computer-readable medium storing computer readable instructions executed by a computer causes the computer to perform the steps of:
 receiving directly from a plurality financial institutions, on an ongoing basis, customer transaction data and customer account data associated with customers of the financial institutions, and counterparty transaction data associated with counterparties that are entities with whom the customers transact;   monitoring the customer transaction data, the customer account data and the counterparty transaction data to assess risk associated with the customers, segments of customers and the counterparties by causing the computer to (1) determine behavioral baseline scores for (i) the customers, (ii) the segments of customers and (iii) the counterparties based at least in part on the customer transaction data, the customer account data and the counterparty transaction data, including frequency of transactions, frequency of use of financial institution channels, transaction amounts and account balances, wherein each of the behavioral baseline scores is customer-specific, segment of customers-specific or counterparty-specific and indicates normal risk in terms of financial transaction behavior for the customer, the segment of customers and the counterparty, and (2) determine a behavioral baseline score deviation, wherein the behavioral baseline score deviation indicates either a negative deviation of the behavioral baseline score indicating potentially risk inducing behavior or a positive deviation of the behavioral baseline score indicating potentially risk reducing behavior; and   initiating one or more risk management actions based on the risk associated with the customers including generate and initiate communication of an alert associated with the behavioral baseline score deviation.   
     
     
         22 . The computer readable medium of  claim 21 , wherein the step of receiving further comprises receiving, from the plurality of financial institutions, at least one of product data, account data, channel data or customer data and wherein the step of monitoring further comprises monitoring the at least one of product data, account data, channel data or customer data to assess the risk. 
     
     
         23 . The computer readable medium of  claim 21 , wherein the step of receiving further comprises receiving, from the plurality of financial institutions, at least one of asset data or liability data, and wherein the step of monitoring further comprises monitoring the at least one of the asset data or the liability data to assess the risk. 
     
     
         24 . The computer readable medium of  claim 21 , wherein the step of receiving further comprises receiving, from the plurality of financial institutions, negative data and wherein the step of monitoring further comprises monitoring the negative data to assess the risk. 
     
     
         25 . The computer readable medium of  claim 21 , wherein the step of receiving further comprises receiving, from the plurality of financial institutions, claims data and wherein the step of monitoring further comprises monitoring the claims data to assess the risk. 
     
     
         26 . The computer readable medium of  claim 21 , wherein the step of receiving further comprises receiving, from a plurality of non-financial institutions, non-financial institution data and wherein the step of monitoring further comprises monitoring the non-financial institution data to assess the risk. 
     
     
         27 . The computer readable medium of  claim 25 , wherein the step of receiving further comprises receiving the non-financial institution data, wherein the non-financial institution data includes one or more of transaction data, product data, account data, channel data, customer data, negative data, counterparty data, asset and liability data, or claims data. 
     
     
         28 . The computer readable medium of  claim 21 , wherein the computer readable instructions executed by the computer causes the computer to perform the further step of identifying an emerging risk type based at least in part on the customer transaction data or the counterparty transaction data. 
     
     
         29 . The computer readable medium of  claim 28 , wherein the computer readable instructions executed by the computer causes the computer to perform the further step of determining a risk score for the customers, the segments of customers and the counterparties, wherein the risk score is associated with one or more risk types and is based on risk patterns and the customer transaction data or counterparty transaction data, wherein the risk score indicates a likelihood of a negative financial impact when doing business with an associated customer, segment of customer or counterparty based on one or more specific types of risk. 
     
     
         30 . The computer readable medium of  claim 21 , wherein the step of monitoring further comprises monitoring at least the customer transaction data to identify suspicious activities potentially associated with identity misappropriation incidents.