Difference between revisions of "Finance & Accounting"
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* [[Backtesting]] | * [[Backtesting]] | ||
* [http://deepindex.org/#Games Deepindex.org list] | * [http://deepindex.org/#Games Deepindex.org list] | ||
| + | * [http://www.federalregister.gov/documents/2021/03/31/2021-06607/request-for-information-and-comment-on-financial-institutions-use-of-artificial-intelligence Request for Information and Comment on Financial Institutions' Use of Artificial Intelligence, Including Machine Learning] A Notice by the Comptroller of the Currency, the Federal Reserve System, the Federal Deposit Insurance Corporation, the Consumer Financial Protection Bureau, and the National Credit Union Administration on 03/31/2021 | ||
* [http://www.dataserv.com/solutions/3-way-match Touchless Invoice Processing with AutoVouch] | * [http://www.dataserv.com/solutions/3-way-match Touchless Invoice Processing with AutoVouch] | ||
* [http://emerj.com/ai-sector-overviews/facial-recognition-in-finance-current-applications/ Facial Recognition in Banking – Current Applications | Niccolo Mejia] | * [http://emerj.com/ai-sector-overviews/facial-recognition-in-finance-current-applications/ Facial Recognition in Banking – Current Applications | Niccolo Mejia] | ||
Revision as of 17:49, 6 June 2021
Youtube search... ...Google search
- Case Studies
- Improper Payments
- Backtesting
- Deepindex.org list
- Request for Information and Comment on Financial Institutions' Use of Artificial Intelligence, Including Machine Learning A Notice by the Comptroller of the Currency, the Federal Reserve System, the Federal Deposit Insurance Corporation, the Consumer Financial Protection Bureau, and the National Credit Union Administration on 03/31/2021
- Touchless Invoice Processing with AutoVouch
- Facial Recognition in Banking – Current Applications | Niccolo Mejia
- AI in Accounting: How Artificial Intelligence & Machine Learning Technology Has Changed the Industry | Faith Kubicki
- FINRA Technology | Dmytro Dolgopolov
- AI and ML in Financial Services Compliance Management: Use Cases for the Regulators
- Hitachi's H big data analysis engine; determine correlations, which can inform business decisions, forecasting, fraud detection, pricing | Bernard Marr - Forbes
- AI Startups in Auto Lending – 2 Well-Funded Examples | Niccolo Mejia - Emero
- Areas where AI can contribute to compliance efficiency and effectiveness. | Pinchas These include:
- NLP solutions can ‘read’ documents and perform a range of tasks including extracting metadata, identifying entities that are referred to, and ‘understanding’ the intent or purpose of specific parts of the document.
- Know Your Customer Process – AI’s ability to analyze the vast amount of data and find patterns can create a more streamlined KYC process. By using algorithmic machine learning models, firms can generate risk profiles of individuals in just minutes.
- Money Laundering Detection – Using AI, firms can evaluate monitoring reports, news items, and regulatory alerts, and further analyze those that indicate the highest risk exposures.
- Rogue Employee Detection -AI can be used to identify employees that create fake accounts by tracking multiple accounts using the same email or IP address.
- Trade Monitoring – Through AI, regulatory bodies can learn traders’ personalities and behavior which increase the precision of identifying suspicious trading.
