Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

AI ni Credit Underwriting adanwo

Review credit underwriting, proxy discrimination, adverse-action notices, alternative data, explanations, and regulatory limits.

Jẹmọ itọsọna awọn ọnaAI Ni Kirẹditi Underwriting
Ibeere 1 ti 8

Kini awoṣe kikọ-kirẹditi AI ni akọkọ gbiyanju lati ṣe asọtẹlẹ?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini awoṣe kikọ-kirẹditi AI ni akọkọ gbiyanju lati ṣe asọtẹlẹ?
    • Awọn iṣeeṣe ti oluya yoo aiyipada
    • Ile-ifowopamọ ayanfẹ ti oluyawo
    • Itọsọna ọja iṣura
    • Ọjọ ori oluya ni pato
  2. Kini 'iyasọtọ aṣoju' ni awọn awoṣe kirẹditi?
    • Lilo olupin afẹyinti
    • Ẹya kan ti o duro fun abuda ti o ni aabo bi ije
    • Igbanisise aṣoju lati beere fun awọn awin
    • Encrypting onibara data
  3. What does an adverse-action notice communicate to a credit applicant?
    • A guarantee of future loan approval
    • A credit score that proves discrimination
    • That the creditor took an adverse action on the credit application
    • A list of every variable in every model
  4. Tani o le ṣe kikọ silẹ AI ti o le ṣe iranlọwọ ti awọn kaadi Dimegilio ibile nigbagbogbo kọ?
    • Nikan ni oro oluyawo
    • Awọn ile-ifowopamọ funrararẹ
    • Awọn olutọsọna ijọba
    • Awọn olubẹwẹ 'Tinrin-faili' pẹlu itan-kirẹditi ibile kekere
  5. Kini idi ti awọn irinṣẹ bii SHAP ṣe lo ninu kikọ kikọ kirẹditi?
    • Lati ṣe ipinnu ipinnu si awọn ẹya kan pato fun awọn alaye
    • Lati yara lori intanẹẹti
    • Lati ṣeto oṣuwọn iwulo akọkọ
    • Lati wa awọn virus kọmputa
  6. Kini ẹdọfu aringbungbun ni lilo data diẹ sii fun kikọ silẹ?
    • Awọn data diẹ sii nigbagbogbo dinku išedede
    • Awọn data diẹ sii le mu iṣedede pọ si ṣugbọn o le tọju iyasoto
    • Data jẹ arufin lati lo
    • Data jẹ ki awọn awin ni ọfẹ
  7. When a creditor supplies a statement of reasons under Regulation B, what must those reasons describe?
    • A generic statement that an algorithm denied credit
    • A complete source-code listing
    • Specific principal reasons that accurately describe factors actually used in the decision
    • A promise that the applicant will later be approved
  8. Why can an alternative-data source raise fairness questions in underwriting?
    • It is always illegal to use any nontraditional data
    • It guarantees a higher credit score
    • It eliminates the need for validation
    • It may act as a proxy for protected characteristics or reflect uneven coverage