Text analysis2026
FlowX.AI · USA and Romania
A family of small models that pull amounts, deal parties, jurisdictions, accounts and sanctions-screening mentions out of banking and insurance texts. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Extracting amounts, parties and jurisdictions from operation descriptions
- Parsing customer due diligence material and sanctions lists
- Marking account numbers and payment details in documents
- Sizes
- about 150M (based on ModernBERT-base)
- Hardware
- from: Laptop
Text analysisGGUF2026
FlowX.AI · USA and Romania
Small models from a banking software company that read the text of an SMS, email or chat message and explain which scam tactic it uses. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Flagging incoming messages that show signs of deception
- Explaining which phrase triggered the flag
- Filtering emails and messages before they reach an operator
- Sizes
- 0.6B – 1.7B
- Hardware
- from: Laptop
Finance2024–2026
MaLA Lab, Singapore Management University (with University of Technology Sydney) · Singapore
A pretrained model that finds anomalous nodes in a relationship graph and marks suspicious vertices on a new graph without training on it. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Finding suspicious accounts and counterparties linked to each other
- Highlighting atypical participants in a payment graph
- Testing hypotheses about ring schemes and chains of intermediaries
- Sizes
- weights file about 1.5 MB
- Hardware
- from: Laptop
Finance2025
Tsinghua University (NeoQuasar) · China
A foundation model for market candlestick data: trained on data from more than 45 exchanges, it forecasts prices and volumes. The largest version, large, is not open.
- Forecasting candlesticks and trading volumes
- Volatility estimation
- A base for fine-tuning on your own series
- Sizes
- 4.1M – 102M
- Hardware
- from: Laptop
Cybersecurity2022–2025
NVIDIA · USA
An open NVIDIA bundle: a card-transaction graph plus a classifier, a profile of normal user behaviour, log parsing and root-cause search. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Highlighting suspicious card operations taking participant links into account
- Tracking deviations of an account from its usual behaviour
- Parsing logs and finding the root cause of an incident
- Sizes
- from a few megabytes (tree-based models) up to BERT-based classifiers
- Hardware
- from: 1 GPU
Finance2025
The Fin AI · international project
Models that spell out their reasoning before answering financial questions with numbers and tables. Not investment advice: decisions are made by a specialist.
- Calculation questions on statements with the working shown
- Working through tasks with tables and numbers from documents
- Checking calculations made by hand
- Sizes
- 8B и 14B
- Hardware
- from: 1 GPU
Finance2025
Shanghai University of Finance and Economics (SUFE) · China
A reasoning model for financial tasks based on Qwen2.5-7B: calculations, report analysis, regulatory questions. Trained on Chinese and English data.
- Financial calculations with step-by-step explanations
- Answering questions about financial statements
- Analyzing tables of financial data
- Sizes
- 7B
- Hardware
- from: Laptop
Finance2023–2024
Du Xiaoman (Duxiaoman-DI) · China
A large Chinese model family for the financial industry: advice, document reading and long texts up to 8k-16k. Not investment advice: decisions are made by a specialist.
- Answering customer questions about banking products
- Working through long financial documents
- An internal assistant for a finance company regulations
- Sizes
- 6B – 176B
- Hardware
- from: 1 GPU
Finance2023–2024
The Fin AI / ChanceFocus · international project
One of the first open model families for financial text: reading statements, news and questions about numbers. Not investment advice: decisions are made by a specialist.
- Reading financial statements and press releases
- Answering questions about numeric data in documents
- Classifying financial texts
- Sizes
- 0.5B – 30B
- Hardware
- from: Laptop
Finance2023–2024
AI4Finance Foundation · USA
An open set of lightweight add-ons for ordinary language models that work with financial texts and news. Not investment advice: decisions are made by a specialist.
- Assessing the tone of financial news and reports
- Tagging mentions of companies and instruments in text
- Preparing digests from a stream of business news
- Sizes
- adapters for 6B - 20B base models
- Hardware
- from: 1 GPU
Finance2024
MIT and MIT-IBM Watson AI Lab · USA
Models that label suspicious chains of transfers by looking at who sent the money at the start of the chain and who received it at the end. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Classifying transfer chains as ordinary or needing review
- Finding new suspicious chains in a large flow of operations
- Preparing material for the compliance team
- Sizes
- checkpoints from 0.1 to 45 MB
- Hardware
- from: Laptop
FinanceNot maintained2024
Writer · USA
A large model for financial documents with a context window of about 131k tokens: it holds long reports whole. Not investment advice: decisions are made by a specialist.
- Working with long annual reports and prospectuses
- Summaries and digests of financial documents
- Finding answers inside a large document pack
- Sizes
- 70B (the model card states 72 billion parameters)
- Hardware
- from: Cluster
FinanceGGUFNot maintained2023
AdaptLLM · not disclosed
Finance-tuned versions of Llama 2: reading industry texts, reports and questions about terminology. Not investment advice: decisions are made by a specialist.
- Reading financial news and reports
- Answering questions about financial terminology
- A base for fine-tuning to your own financial task
- Sizes
- 7B и 13B
- Hardware
- from: Laptop
FinanceNot maintained2023
Fudan-DISC, Fudan University · China
A financial assistant made of several fine-tuned experts: advice, calculations, document reading and knowledge-base search. Not investment advice: decisions are made by a specialist.
- In-house advice on financial questions
- Reading financial documents and news
- Prompts for front-office staff
- Sizes
- 13B
- Hardware
- from: 1 GPU
FinanceNot maintained2020
Prosus · Netherlands
A classic model that determines the tone of financial news: positive, negative or neutral. English only, runs fast on a CPU.
- Scoring the tone of company news
- Labeling reports and press releases
- Signals for analytics dashboards
- Sizes
- 110M
- Hardware
- from: Laptop
