Start hereDictionarySearch
English
日本語English中文ItalianoFrançaisDeutschEspañol
Sign in Fees and how to study
NOMISMA
Read history Money history: the world and JapanBubbles, frauds and financial crisesPeople who changed moneyReadingsSee all of Read history
Understand how money works Banks and depositsInterest, prices and exchange ratesCash and digital paymentsHistory's lessons for today's decisionsSee all of Understand how money works
Look at coins Browse by photoImages, letters and materialsCare, storage and tradingSee all of Look at coins
Start hereDictionarySearch
Fees and how to study Sign in
Language
日本語English中文ItalianoFrançaisDeutschEspañol
BIBLIOGRAPHY

Annual Economic Report 2024, Chapter III: Artificial intelligence and the economy: implications for central banks

Bank for International Settlements

Claims Supported by This Source

  1. Finance is one of the sectors with the greatest opportunities and risks from AI. The impact is demonstrated in four areas: payments, lending, insurance, and asset management.
  2. AI increases efficiency in back-office operations, regulatory compliance, fraud detection, and customer service. Prime examples are improvements in KYC (Know Your Customer) and fraud detection.
  3. In lending, creditworthiness can be assessed using alternative data, such as bank account transactions and payment records for rent, utilities, and communication fees, as well as non-financial data like educational background.
  4. In a survey cited by the BIS, about 70% of financial services firms worldwide use AI for cash flow forecasting, credit scoring, and fraud detection.
  5. New risks: proliferation of cyberattacks such as phishing; bias and discrimination (entrenching disparities in access to credit); dependence on a few AI providers; and financial stability risks from reliance on the same algorithms amplifying herding behavior, liquidity hoarding, bank runs, and fire sales.
  6. In a representative survey of U.S. households, trust in generative AI is lower than in services handled by humans in critical areas like banking and public policy.
  7. Central banks can also use AI for compiling statistics, analysis, supervision, and payment system oversight. In the BIS's Project Aurora, machine learning outperformed rule-based money laundering detection.

How to read the verification levels. 'Read' means the material was read in its entirety. 'Bibliography and abstract confirmed' means the bibliographic information and abstract were verified. 'Via course material' means the source was referenced through the main text of a book or a country's monetary history. The text hash is the SHA-256 of the main text from the original source page, excluding tags and whitespace, used to detect if the material has been altered.