Common Use Cases
We approach solutions by examining how new sources of information can be used to improve revenue and how analytics can reduce total cost of operations. We use data science and machine learning tools to improve efficiency, optimize portfolios, and provide real-time snapshots. Our ability to execute on common financial services features financial forecasting models, analyzing customers, aggregating market and consumer data, and mitigating fraudulent behavior.
SFL Scientific leverages data science tools to create custom platforms and systems that monitor, learn, and inform our clients. With many new tech areas emerging into the market, we're here to minimize the risk with innovation and generate immediate return. We also work with asset management and hedge fund customers to help run experiments, aiding and enabling quantitative researchers and finance data scientists.
Predictive analytics can be applied to a host of unstructured data, combining predictions from sentiment, traditional data sources, and models. Developing models for forecasts, market analysis, and trends, or simulating patterns. Since business cases are client specific, some of our latest work is below:
Credit Ratings: Analyzing continuous feeds real-time data for credit ratings providing lenders more accurate snapshots of a customer’s assets, business, operations, and transaction history.
Tracking Tools: Develop tracking tools to mine and report key metrics, new reports, release for target companies, products, segments, and industries. Analyze millions of data points across the market and consumer base to determine the correct investment strategy.
Volume Analysis: We can implement volume examination models to make pricing predictions. Similar models examine social and search traffic, identifying online precursors for stock market moves.
Financial Analysis: We can analyze and create decision platforms for payments, credit providers, and institutional traders, integrating historical and real-time data transactional data to improve efficiency.
Detect and prevent fraud (e.g. transactions, credit card fraud, phishing) by leveraging advanced analytics to predict anomalies in real time.
Graph Analysis: Create machine learning security systems to track patterns and assess in real-time threats and fraud. Monitor user profile information, historical data, credit data, and access point information to predict risks and automate action.
Financial Fraud: We can analyze account balances, spending patterns, credit history, employment details, location, and other information to determine if transactions are legitimate and automate their handling.
Credit: Combine segmentation tools with predictive analytics to determine the return on credit and loans. Ingest location, neighborhood development, social, credit reports, business reports, transaction history, demographics, and historical data to make predictions.
Audit: Improve data management and deploy new generation analytics on your existing systems (or employ a cloud strategy) to improve fraud and criminal activity detection. Visualize in real-time, with aggregated risk data, risk models and analysis.
We apply natural-language processing, logic, text analysis and summarization to examine media, documents, and external data to discover what folks really think.
Algorithms: Build tools around market sentiment data (e.g., Twitter feeds) for real-time and in-depth trend and news analysis.
Target Tracking: Create algorithms to track trends, monitor the launch of new products, respond to issues, and measure improvements in overall brand perception.
Operations: Analyze unstructured voice recordings from call centers, improve customer service, and optimize churn, up-sell, cross-sell, and products. Filter fraud and prevent unauthorized access.