Bank of America Corporation

Wells Fargo Bank, N.a.

Daniel Kern

Adam Z Sun

David A Hogeboom

Misty Ritchie

Matthew R Quinn

Peter C Richards

Pasha M Maher

Joan F Pilsmaker

Qiyi Lu

Anne Bromstead

Rogelio Olarte Bautista, Jr

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of managing regulatory questions. The systems and methods receive a question having words and phrases. The systems and methods identify keywords in the question using a knowledgebase. The systems and methods determine closely related questions based on the identification, the closely related questions having answers associated with each question of the closely related questions. The systems and methods perform machine learning on the answers of the determined closely related questions.

Identification and management of frequently asked questions

A network security assessment apparatus includes a memory and a processor. The memory stores first, second, and third values of a metric. The metric indicates one or more of a number of network security breaches and a number of fraudulent transactions. The processor chronologically orders the first, second, and third values for the metric over a period of time and compares the first, second, and third values against a threshold to produce first, second, and third results. If the first result is different from the second result or if the second result is different from the third result, the processor increments a volatility count. The processor determines, based on the volatility count, that a control should be implemented to mitigate one or more of the number of network security breaches and the number of fraudulent transactions and in response to that determination, the processor implements the control.

Network security assessment system

An intrusion assessment apparatus includes a memory and a processor. The memory stores first and second records and first, second, and third keywords. The processor determines a number of occurrences of the first, second, and third keywords in the first and second records and assigns the first record to a first cluster and the second record to a second cluster. The processor also determines a per-record average number of occurrences of the keywords in a plurality of records assigned to the first cluster and in a plurality of records assigned to the second cluster and receives a search request indicating the keywords and an emphasis value for each keyword. The processor also determines that the first cluster should be returned in response to the request and transmits, based on that determination, the first record.

Intrusion assessment system

According to embodiments of the present disclosure, a keyword frequency analysis system stores a plurality of sets of records. Each set of records may be associated with a dimension and may comprise a first keyword and a second keyword. The system may also receive the plurality of sets of records, determine a frequency of the first keyword in each set of records and determine a frequency of the second keyword in each set of records. The system may further determine an expected frequency of the first keyword in a first set of records associated with a first dimension, based on the frequency of the first keyword and the frequency of the second keyword. The system also compares the frequency of the first keyword and the expected frequency and, based on the comparison, determines whether the first keyword is either overrepresented or underrepresented in the first set of records.

Keyword frequency analysis system

Unstructured data is received from a plurality of sources to facilitate risk analysis. The unstructured data comprises a plurality of bodies of text. Each body of text from the unstructured data is deconstructed into individual terms. The individual terms from each body of text are converted into a structured form. The individual terms in the structured form are categorized according to a comparison of the structured form to another structured form. The individual terms in the structured form are quantified according to at least the categorization of the individual terms.

Risk analysis using unstructured data

Systems and methods for calculating financial crimes ratings for banking entities are provided. High risk transaction data for a plurality of similarly situated banking entities is collected. The banking entities may include banking centers, business customers, individual customers or other banking entities. Summary data is then calculated from the high risk transaction data. The summary data for a banking entity is then compared to summary data of the similarly situated banking entities to calculate the rating. Financial crimes ratings may be used to allocate investigation and enforcement resources.

Statistical prioritization and detection of potential financial crime events

According to one example, a test portfolio optimization system includes a test repository, an activity repository, an exception repository, and an optimization engine. The optimization engine is operable to generate a plurality of test portfolios. Each test portfolio comprises different types of tests of a plurality of tests. The optimization engine is also operable to determine a test cost for each portfolio over the period of time. The test cost for each portfolio is equal to a sum of costs for each test type within the portfolio over the period of time. The optimization engine is also operable to determine an impact per portfolio period of time. The impact per portfolio is equal to a sum of impacts per test type for each test type over the period of time.

Test portfolio optimization system

Systems and methods for determining the likelihood that a group of transactions may be structured to avoid a limit or reporting requirement, such as a government reporting requirement, are disclosed. The frequency distributions of a customer's transactions for different value ranges are compared to determine whether transactions within a target range occur randomly or at an unexpected level. In another embodiment, the frequency distribution of a customer's transactions is compared to a frequency distribution created by randomly sampling a distribution of similar transactions to determine whether the customer's transactions occur randomly.

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Charlotte, NC, United States of America

Daniel Kern