Apply continuous learning

Add historical decisions

To use the Continuous Learning Data Store (CLDS, continuous learning, or decision reapplication) feature and reapply previous decisions, you must add the decisions to the Control Tower Data Stores.

Run Ingest Historical Decisions For Continuous Learning Business Process

The Ingest Historical Decisions For Continuous Learning Business Process (BP) bundled with Tara is used to reapply decisions.

In the BP, there are only two steps:

• Fetch historical decisions
• Save historical decisions

tip

To run the BP as frequently as needed automatically, create a schedule. For more details, see Schedule Business Process.

Fetch historical decisions

The step locates historical decisions from S3 and converts them into a CSV file appropriate for Tara to save the decisions to the relevant Data Stores. The exact implementation of the step varies depending on your way of storing historical decisions. A resulting CSV file is uploaded to S3 and outputs the URL for the next step.

The expected FUF columns in the historical file are as follows:

HIT_MESSAGE_ID = "SYSTEM_ID";
HIT_TAG_NAME = "TAG";
HIT_TEXT = "LIST_MATCHING_STRING";
HIT_TYPE = "HIT_TYPE";
HIT_WLE_CITY = "CITY";
HIT_WLE_COUNTRY = "COUNTRY";
HIT_WLE_ENTRY_ID = "ID_LIST";
HIT_WLE_NAME = "RECORD_MATCHING_STRING";
HIT_WLE_PRIMARY_NAME = "NAME";
HIT_WLE_STATE = "STATE";
HIT_WLE_TYPE = "TYPE";
MESSAGE_ANALYST_DECISION = "T_DECISION_TYPE";
MESSAGE_ANALYST_NAME = "T_LASTOPERATOR";
MESSAGE_CONTENT = "T_MESSAGE";
MESSAGE_DECISION_DATE = "T_COMPLETED";
MESSAGE_ID = "T_SYSTEM_ID";
MESSAGE_ORIGINAL_FORMAT = "T_NATURE";
MESSAGE_ORIGINAL_SUBTYPE = "T_TYPE";

Mind the requirements for naming historical files:

• Names of message files must start with messages followed by a unique identifier such as date, for example, messages-01012024.

• Names of hit files must start with hits followed by a unique identifier such as date, for example, hits-01012024.

• A unique identifier must be the same in both file names.

• The supported file format is CSV.

The converted CSV file columns are as follows:

"id",
"hit_id",
"content",
"contentformat",
"gold_decision",
"gold_decision_comment",
"gold_decision_date",
"analyst_name"
"hit_tagname",
"hit_hittext",
"hit_hittype",
"type",
"subtype",
"hit_sanctiondata_name"
"hit_sanctiondata_type",
"hit_sanctiondata_primaryname",
"hit_sanctiondata_country",
"hit_sanctiondata_city",
"hit_sanctiondata_state",
"hit_sanctiondata_sourceentryid" 

Save historical decisions

The step batch-loads decisions to the relevant Data Stores using a URL to the Tara-formatted CSV file. All sensitive data in the Data Stores is hashed and does not contain any readable information. All historical data is saved in two tables linked by the reapplication ID.

The following hit-level information is hashed:

• TAG_NAME
• HIT_TEXT
• HIT_TYPE
• SANCTION_DATA_NAME
• SANCTION_DATA_PRIMARY_NAME
• SANCTION_DATA_COUNTRY
• SANCTION_DATA_ENTRY_ID

The alert-level reapplication data columns in the pss_reapplication_data_v1 Data Store are as follows:

Data column	Description
id	Reapplication ID.
msg_id	Original alert ID.
message_json	Original payment content converted into a map of tags versus values. The values are hashed.
msg_format	Original payment content format. Currently, the FUF value is supported.
analyst_name	Name or ID of the analyst who made the final decision.
decision_date	Date when the final decision was made.
message_decision	Analyst's final decision.
message_narrative	Analyst's final decision comment.

The hit-level reapplication data columns in the pss_reapplication_hashcodes_v1 Data Store are as follows:

Data column	Description
reapplication_data_id	Link between two tables
hit_hash_value	Hashed sum of hit parameters

Record data analysis

Each time you upload historical data to the Data Stores, new records are added to the pss_dr_file_analytics_data_v1 Data Store.

The information is stored in the following columns:

Data column	Description
filename	Name of the processed file. If an upload fails, the field also contains the reason for the failure.
load_date	Timestamp of upload.
status	Status of execution: SUCCESS or FAILED.
number_alerts_total	Total number of alert-level records from the message CSV file.
number_alerts_loaded	Number of successfully loaded alert-level records from the message CSV file.
number_hits_total	Total number of hit-level records from the hit CSV file.
number_hits_loaded	Number of successfully loaded hit-level records from the hit CSV file.

Apply continuous learning

The Continuous Learning bot step is only enabled for the FUF tag content format messages. If the alert content is not in the FUF format, the bot step outputs a "decision_reapplied" signal with the FALSE value, and the Business Process continues to run through all the steps. If a decision is reapplied ("decision_reapplied signal" == TRUE), the workflow execution skips all ML and data manipulation steps and goes directly to the Generate report Bot Task where it records the decision reapplication and all relevant data and sends out a response as a decision object fully populated with alerts and hits. For more details, see Overview | Core Business Process.

Identify candidate for continuous learning

Tara fetches all the decisions analysts make on alerts that match the incoming request. The initial match is based on hashed unique fields and the number of hits in the alert.

The following fields are hashed and must match a historical alert from the Decision Reapplication Data Store:

• TAG_NAME
• HIT_TEXT
• HIT_TYPE
• SANCTION_DATA_NAME
• SANCTION_DATA_PRIMARY_NAME
• SANCTION_DATA_COUNTRY
• SANCTION_DATA_ENTRY_ID

For example, you have the following hit:

{
  "hit_tagcontent": "bank of atlantida",
  "hit_tagname": "ORGR_NAME",
  "hit_hittext": "atlantida",
  "hit_hittype": "NAME",
  "hit_sanctiondata_name": "Atlantida INC",
  "hit_sanctiondata_primaryname": " Atlantida Solutions INC",
  "hit_sanctiondata_country": "VUT",
  "hit_sanctiondata_sourceentryid": "12546",
}

Tara normalizes the values and concatenates them to generate a hit key. The hit key is then hashed, and Tara queries the Data Store for a matching hit key. This query returns a list of alerts that are good for reapplication within the configured timeframe. For more details, see Install and configure | Configure.

Compare FUF tags

When a query returns a list of similar alerts from a historical table, a per-FUF tag comparison is performed. Tara uses configured tags as input for the comparison. If you provide no tags, the decision reapplication is not applied (more tags to compare means less chance for reapplication). If the incoming FUF content does not have matching tags, continuous learning is not applied.

All historical records are stored in a Data Store, and the original FUF message has its values hashed for the PII protection. The comparison with the current request is done on the hashed value basis per field, so no PII is stored in the raw format.

See sample payment content in Data Store

{
  "VALUEDATE": "f9df48ebdfc2d576729bfb7e9e1e956c155534b3f5e4f8aebbd5ea2923c84584",
  "APPLI": "4bc968e78749c8a201d1e51680b36a842f3499fa4a3e719f1e9bf32bb7821874",
  "MSGAMOUNT": "342e489174cc8579d038ea97683b010fee86de2c274d2a2eafcb595b213e643f",
  "BUSINESS": "11d40417959631d3d2420e8cd8709893c11cd7a4db737af63e8d56cfa7866f85",
  "PYMTDTLS": "3633fe22c5b852b1a3f738508d74127669fa0bd9f8873db5b1013d98c2171fe1",
  "BEN_NAME": "2de4e108f9a40c2863416eae468cad624954b45d67d64c3a030814e600848e78",
  "BEN_ADDR": "2320995e4fe697e19c6ef23dd04dc46a77db87081e7e30fdc0ec4419b374921a",
  "USERREF": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
  "BUNIT": "46109ac6e235cdb78b9e0ec1ec19d4b0c6df919e968297d77f3c9a488e116e28",
  "AMOUNT": "28abef94cbe0103801b323d0e0c9e852847f980fc00ef4ff3be8e7f3e4cc515b",
  "BNFCRYREF": "ca0dd26a286c75298d1dca246306965e8bec14fdf32c4f41e531b0c5ee859de4",
  "ORGR_APD": "3ba755fe7dc814ffbe4cbc056cf26ddf31e86e497f8d4480a4e43ba4a18623e7",
  "SEND_FICD": "0fb5783adefdf3168cf7dce238959108aa2ffbbe24c6c167f451ba4aadeb9cf3",
  "REFERENCE": "3cb83b3b870ae03c53ac679a254142cac56ba612d141973ea84c1d69d61fe2bc",
  "MSGSRCTYPE": "d9705d8ee25f3f783a0c0a5e4b15bd7f6279dcfe11fa053504b2c14c5fb2a8b9",
  "SERVICE": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
  "RECV_INFO": "d19ca194aacf0af1e6d7dfc3a066e0e64df3b9e999384f5d2d258cd30e4ade39",
  "BNFCRY": "99d7bef242865b79e7ddef6d16792e399e889c2e8dbde5dd66673d48b7ce4e91",
  "UUMID": "fa3c78b5a50ca497fe784f31eb89f7d206db3ca46d7f0126b2da9ace51a5dc86",
  "TYPE": "1d1db7e3c147afd3f427fa652031b77efc0de7d7869c7eacca972a9daa334529",
  "ORGR_ADDR": "befd7fe00cbf664a95b1c0ce01855a7f264d7fe9edad0526ac2247280e535296",
  "RECEIVER": "0fb5783adefdf3168cf7dce238959108aa2ffbbe24c6c167f451ba4aadeb9cf3",
  "CURRENCY": "d60fbd8718eceb7f7751c68b695e33ef974d0ecca2b1bf20c928e6d1b995f00e",
  "UNIT": "385cfdbc00ec32031699460779c15099b2bba3cad0e440fffb08e10df0acb9e1",
  "MSGINSTNBR": "6b86b273ff34fce19d6b804eff5a3f5747ada4eaa22f1d49c01e52ddb7875b4b",
  "MSGDATE": "5724bebd9b23019941c78f6d13b9f14e01e161b9abac00010f4e3cc55adc0ddc",
  "SENDREF": "3cb83b3b870ae03c53ac679a254142cac56ba612d141973ea84c1d69d61fe2bc",
  "SENDER": "0fb5783adefdf3168cf7dce238959108aa2ffbbe24c6c167f451ba4aadeb9cf3",
  "MSGKEY": "09272a9f73441a50e7bbc3e18300281751c2e516520cf720e1df8e26ed90a627",
  "RECV_FICD": "0fb5783adefdf3168cf7dce238959108aa2ffbbe24c6c167f451ba4aadeb9cf3",
  "MSGTYPECD": "71d6a37fa41aba8789868f45be4dfa89f0ad81e93aca9e8b8f1be8abba972ca4",
  "SEND_INFO": "6561800aaa202f3ab8fa9790db111931a8331dd6d2bf066a62326d81e75b3f18",
  "BEN_APD": "81b6be0394acc3d9888f013a61e7364e1902d80ec50836d68333d7f29bb35530",
  "SRCHDEFID": "ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad",
  "MSGDIR": "3a7470e00c076b678c41c8ae4a4945198a9e243cdb42e044612535facb5f225d",
  "ORIGNTR": "243457eb1cd29d738867d23036e884436f00d0c4f35a1b07fb6a62eaf93b4578",
  "ORGR_NAME": "25c23dd62e4ebfc5c7f9e730bd06909e7fe5cbfdf8f47670d697dd047c37f646",
  "FIRCOSOFT": "c027d7f3740e067819848be6ab32e4b801b635f3fddd3c2c9a07115b04f495e4"
}

While all the message values are hashed, only a subset is considered relevant. This subset is selected when configuring Tara.

If all the relevant values in the incoming message match the candidate, it moves on to the next phase. The list of matched records can be extensive. Thus, only the latest 100 records are kept.

If the list contains both RESOLVE and NO_DECISION adjudications, continuous learning is not applied.

Apply custom rules

The CLDS flow leverages the Drools rule engine and its rule language to choose the analyst decision to be replicated and carried forward.

The rules are stored and retrieved from S3 MinIO. When you upload a Tara bundle to Control Tower, place the default file with rules (reapplicationRules.drl) to the doc-upload/sanctions/{$tara_version}/rules repository. You can change the rules and replace the default file but make sure the filename stays the same.

The reapplication data list contains only resolved records within the configured timeframe.

A sample rule is as follows:

rule "Check for 10 RESOLVED alerts from 3 different analysts"
    dialect "mvel"
    when
        ReapplicationDecisions($dataList: reapplicationDataList)
        // alert has been dispositined at least 10 times
        eval($dataList.size() > 9)
        ReapplicationData($decisionDate: decisionDate) from $dataList
        eval($decisionDate != null && $decisionDate.isAfter(LocalDateTime.now().minusMonths(6)))
        $distinctAnalystNames: java.util.Set() from accumulate (
           ReapplicationData($distinctAnalystName: analystName) from $dataList,
           collectSet($distinctAnalystName)
        )
        // at least three different analyst names
        eval($distinctAnalystNames.size() > 2) 
        $sameDecision: java.util.Set() from accumulate (
           ReapplicationData($messageDecision: messageDecision) from $dataList,
           collectSet($messageDecision)
        )
        eval($sameDecision.size() == 1) // All decisions are the same
    then
        ReapplicationData rd = $dataList.get(0);
        decisionReapplication(rulesAnswer, rd.messageDecision, $dataList);
end

The rule engine receives a ReapplicationDecisions object that contains a list of the ReapplicationData objects, based on which you can create rules to return a decision and explanation in the form of a RulesAnswer object.

For more details on rules, refer to Make decisions.