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Analyzing Financial Networks
Financial networks are dense and opaque.
Large financial institutions have hundreds or thousands of subsidiaries, issue hundreds or thousands of securities, and are broadly and tightly interconnected to thousands of institutions through millions of transactions and relationships.
Corporate level information and tools are readily available to analyze companies and markets. But much of the risk and opportunity stems from relationships within and across companies that are far more difficult to understand.
For instance:
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Financial counterparties may transact through multiple entities and complex chains. |
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Intercompany lending and joint ventures can mask hidden assets and liabilities as well as conflicts of interest. |
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Large groups may have multiple issuers and guarantors, as well as cross-company receivables and payables. |
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Complex transaction can conceal critical ownership, counterparty risk and off-balance sheet exposure. |
Traditional methods of representing corporate relationship and transaction data fall
short in their ability to analyze the complex world of corporate interaction. Much of
this process is largely or semi-automated. Data must be integrated from disperate silos and complex network relationships must be forced into hierarchical structures. Along the way, errors are introduced and nuances are lost.
In order to understand financial networks, we need better tools.
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