{
  "fetched_at": "2026-10-19T09:35:06Z",
  "source_url": "https://www.kaggle.com/competitions/hack-for-good-acams-vegas-2023/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Problem Statement: Teams must detect as many suspicious accounts and transactions based on AML and Sanctions typologies that are hidden in the datasets provided.\n\nParticipants will analyze transactional data from four banks across two (2) jurisdictions for each bank, identifying potential AML and Sanctions red flags. The focus initially will be on Bank 1 (BNK65711955). To filter the customer data, participants may have to join the target dataset against Customer_Account dataset on CUSTOMER_ID to get the BANK_ID. Teams will then extend their scrutiny to the other three banks, analyzing the entire dataset to see if the extended data corroborates or enhances the identified red flags in Bank 1, and vice versa.\n",
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    {
      "name": "Evaluation",
      "content": "Evaluation will be done by the judging panel during the event.",
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    {
      "name": "rules",
      "content": "Will be shared separately via email",
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    },
    {
      "name": "abstract",
      "content": "In a world where financial criminals continually evolve their techniques, isolated efforts to combat these activities are becoming increasingly insufficient. The key to the next frontier in fighting financial crimes lies in the collaborative sharing of information. Whether it's across different banks, within a single institution across jurisdictions, or between regulators and industry bodies, the power of shared data can unlock a whole new dimension of financial crime detection.\n\nParticipants will analyze transactional data from four banks across two (2) jurisdictions for each bank, identifying potential AML and Sanctions red flags. The focus initially will be on Bank 1 (BNK65711955). To filter the customer data, participants may have to join the target dataset against Customer_Account dataset on CUSTOMER_ID to get the BANK_ID. Teams will then extend their scrutiny to the other three banks, analyzing the entire dataset to see if the extended data corroborates or enhances the identified red flags in Bank 1, and vice versa.\n\nParticipants will be provided with computing power enabling them to work on their solutions using next generation technology including but not limited to:\n- Artificial Intelligence\n- Machine Learning and LLM algorithms\n- API-based solutions \n- Data Encryption and Anonymization techniques\n- Predictive Analytics to model future financial crime trends based on shared data\n\nHere is a quick summary of the event timeline:\n\n**Day 1 (10/2): \n- Hackathon participants check-in at the PwC booth. \n\n**Day 2 (10/3): \n- All participants will present to a panel of PwC judges at their allotted time slot\n- Judges to evaluate and select winner\n\n\n**Day 3 (10/4):  \n- Award Ceremony and prizes awarded\n- Winner gives brief summary of their idea\n\n\n",
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}