We use leading scientific open source platforms to develop our core technology focused on natural language processing of financial domain data with machine learning, deep neural networks, and knowledge graphs.
We have deeply integrated quantitative factor models, risk analytics, scenario and backtesting capabilities.
Our platform services are underpinned by cutting-edge infrastructure services designed to be ultra-fast, petabyte scale and distributed.
Our solutions meet modern bank infrastructure requirements with on-premise, hybrid and cloud deployments, an enterprise integration layer that takes less than a week to integrate with thousands of enterprise applications and 256-bit AES encryption in flight and at rest.
We process large volumes of structured and unstructured data on a highly scalable basis.
Machine learning and natural language processing of unstructured real-time market data, news, and research.
Leveraging small and big data processing – achieving accuracy of 86.7% on hold-out test financial domain data with and error of +/- 2%. Training data accuracy is in the order of 92.7% for financial Q&A datasets.
The ForwardLane Intelligence engine can craft better conversations compared to traditional rules based conversational bots. We use statistical inference and predictive analytics on large data sets, coupled with conversational memory and knowledge graph a to deliver a more accurate and personalized conversational experience.
Our engine can integrate existing analytical models your firm has built and deliver deeper insights to personalize the client engagement interactions. We enrich data from the graph, integrate demographics, trading history, predictive models:
– understand and visualize relationships
– from neural cloud
– demographic, interests
Hardened, Production-ready infrastructure — using Industry-standard platform services trusted by IBM, GE, HP Enterprise and SAP, ForwardLane’s APIs and Microservices are managed for deployment at scale in enterprise production environments. Full flexibility for deployment via secure cloud, hybrid or on-premise.
Our data is enriched with data harvested from neural networks and machine learning algorithms.
We process large volumes of structured and unstructured data using NoSQL data lake for ingesting and munging sell-side research, real-time news, asset prices, time series data, product data on 35,000 companies in 450 markets with equity, commodities, fixed income, foreign exchange and money market data as well as mutual funds, ETFs, hedge funds and indices.
Peta-byte scale, ultra-fast distributed data store leveraging technologies based on Hbase.
Processing billions of data points at terabyte scale in seconds enables ForwardLane to make sense of data fast.
We deliver unique insights into complex inter-relationships across markets, events, clients, portfolios and other factors leveraging knowledge graph representations, visualization and industry leading neural networks.
As members of the Enterprise Data Management Council, we have integrated FIBO, the Financial Industry Business Ontology standard, with our proprietary ontologies which are peer-reviewed by FINRA-licensed financial experts with over 8000 financial terms.
Simple, efficient, smart – Proprietary search algorithms bring together both structured and unstructured search results tapping into concepts and their relationships by traversing knowledge graphs and federated, distributed data stores.
ForwardLane employs a variety of machine intelligence and learning - primarily natural language processing, deep learning, speech, some vision and bayesian optimization and techniques focused on recommenders and predictive analytics.
We conduct primary research in generative conversation, character-based learning from scratch, very deep convolutional neural networks, and very large classification and Q&A systems.
Financial Industry Business Ontology (FIBO) contributor
Dynamic generated ontologies
Hierarchical conv-nets and re-nets with svm
Topological data analysis
ForwardLane is a member of the Enterprise Data Management Council and contributors to the peer-reviewed Financial Industry Business Ontology. We employ advanced applications of FIBO, in conjunction with our unique flexible dynamic ontologies.
Using logically consistent definitions, we can continuously improve accuracy and be an accurate source trusted by clients.
Pricing, fundamentals on 35,000 companies worldwide
Realtime Machine-readable news – Corporate and Global a specific event news
Market Psych Sentiment Indices quantitatively driven from Twitter and Social Media
Sell-side Embargoed Research
Starmine Analytics – quantitative Analyst Revisions Models which uses NLP, text mining and quantitive scoring to predict bullish and bearish signals
Macroeconomic indicators and data
Morningstar Analyst Ratings – including Morningstar Star Ratings
Daily and Historic Data – Open Ended Funds, Closed Ended Funds, Mutual Funds, ETFs, Commodities
Morningstar Indices – Target date, Country, Allocation, Global Equity, Country (Ex-US)
Holdings level data for funds
Hedge fund pricing data
Department of Labor and Fiduciary standards
Master Reference Data
Global equities pricing for 450 exchanges
Global bond data
FX currency pairs
Demographics, Interests, income brackets, post code, house value