Financial AI

95+

Purpose-built AI models for quantitative finance and trading

Specialized AI models designed for financial applications. From FinBERT sentiment analysis to Chronos time series forecasting, from GARCH volatility modeling to portfolio optimization. Build trading strategies, risk models, and financial analytics entirely in .NET.

Algorithmic Trading Risk Assessment Sentiment Analysis Price Forecasting Portfolio Optimization Fraud Detection Credit Scoring Market Microstructure

Financial NLP

Understand financial text, earnings calls, SEC filings, and market sentiment.

FinBERT

BERT fine-tuned on financial text for sentiment analysis of earnings calls and news.

FinGPT

Open-source financial LLM for robo-advising, analysis, and report generation.

BloombergGPT

Financial domain LLM trained on Bloomberg financial data corpus.

SecBERT

BERT pre-trained on SEC filings for regulatory document understanding.

FinMA

Financial domain instruction-tuned model for complex financial reasoning.

Time Series Forecasting

Predict prices, volumes, and financial metrics with specialized time series models.

DeepAR

Autoregressive RNN for probabilistic forecasting with uncertainty estimates.

Chronos

Foundation model for zero-shot time series forecasting via tokenization.

TimesFM

Google time series foundation model with decoder-only architecture.

N-BEATS

Neural Basis Expansion for interpretable time series forecasting.

N-HiTS

Neural Hierarchical Interpolation for multi-scale temporal patterns.

TFT

Temporal Fusion Transformer with interpretable multi-horizon forecasting.

PatchTST

Channel-independent patch tokenization for long-range time series.

iTransformer

Inverted transformer treating each variate as a token.

Autoformer

Auto-correlation mechanism for long-term time series forecasting.

Informer

ProbSparse self-attention for efficient long sequence forecasting.

Risk & Portfolio Models

Quantitative risk assessment, portfolio optimization, and derivatives pricing.

GARCH

Generalized Autoregressive Conditional Heteroskedasticity for volatility modeling.

VaR Models

Value at Risk estimation with historical, parametric, and Monte Carlo methods.

Black-Scholes

Options pricing model with Greeks calculation.

Monte Carlo

Simulation-based pricing for exotic derivatives and portfolio risk.

Portfolio Optimization

Mean-variance, Black-Litterman, and risk parity portfolio construction.

Factor Models

Multi-factor risk models (Fama-French, Barra) for return attribution.

Financial AI with AiModelBuilder

C# 
using AiDotNet;

// Financial sentiment analysis with AiModelBuilder
var sentimentModel = await new AiModelBuilder<float, float[], float>()
    .ConfigureModel(new FinBERT<float>())
    .ConfigureOptimizer(new AdamOptimizer<float>())
    .ConfigurePreprocessing()
    .BuildAsync(financialTexts, sentimentLabels);

var sentiment = sentimentModel.Predict(earningsCallText);

// Time series forecasting with AiModelBuilder
var forecastModel = await new AiModelBuilder<float, float[], float>()
    .ConfigureModel(new Chronos<float>("chronos-t5-large"))
    .ConfigurePreprocessing()
    .BuildAsync(historicalPrices, futureValues);

var forecast = forecastModel.Predict(recentPrices);

Start building with Financial AI

All 95+ implementations are included free under Apache 2.0.

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