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  "docSlug": "749e1b0275830f32d5e62fdd3d16a7ac",
  "documentTitle": "Sezzle | Results Presentation Deck | 29 slides",
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  "authorName": "Sezzle",
  "documentKindSlug": "conference-presentation",
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  "presentationDate": "2023-02-01 00:00:00",
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  "pageNumber": 9,
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  "notes": "The slide highlights the transition from traditional scoring to a proprietary ML model, showing a 56% decrease in average default probability scores.",
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  "slideHref": "/slides/019de074-6586-72f9-94ef-e38ae12b6fb0/9",
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      "kind": "chart",
      "text": "Average Prophet Score (Probability of Default) from Dec '21 to Jan '23",
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      "text": "The Prophet Model has been a significantly better barometer in identifying high-risk consumers. Since the introduction, the Company has been able to better control risk. RESULT: In 4Q22, Provision for Uncollectible Accounts improved by 232bps.",
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      "text": "A proprietary machine learning model designed specifically for the holiday season. Internally developed throughout FY2022 and deployed during the 2022 holiday period.",
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      "text": "The Prophet Model predicts the probability of default at the time of underwriting. As a result, it does not correlate one-to-one with reported loss rates. Source: Sezzle Internal Data.",
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      "kind": "title",
      "text": "IMPROVED UNDERWRITING WITH NEW RISK MODEL",
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