As competition increases, banks are expected to lose revenue due to customer churn. Natural language processing and automated machine learning generate insights into why customers churn and how to retain them.
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From Data to Application: Darwin's Unique Approach to AutoML
Darwin automates time-consuming tasks ranging from model creation and optimization to model deployment and continuous maintenance.
Garbage In, Garbage Out: Automated ML Begins with Quality Data
Machine learning methods are highly dependent on the quality of the data they receive as input, but data preparation and cleaning can be an unwieldy task, taking up 60% of data scientists' time.
Breaking Away from Cookie-Cutter Algorithms: True Generalization with Evolutionary Methods
Most autoML solutions in the market focus on searching for the best algorithm to fit a given data set. However, these methods lack the ability to produce novel, elegant model architectures.
How to Put Machine Learning Models to Work: Bridging the Gap Between Model Production and Operationalization
Automated machine learning has the potential to reduce the burden on overwhelmed teams by automating the bottlenecks in the data science process. But just having an algorithm isn't enough.
The Darwin Difference: Why Darwin Stands Out From the AutoML Pack
Darwin™️ is an automated model building product that allows you to go from data to model in less time than traditional methods, enabling the rapid prototyping of scenarios and extraction of insights.
Use Case: A Full Framework for Automated Loan Processing
With AMB, lenders can expect an increase in loans offered, with optimized interest pricing and lower defaults, and a forecasted 20% ROI.
Customer Success Story: Predicting Customer Complaints for Telecom
With the power of NLP and auto ML a major telecom provider expects to reduce complaint call volume by 33%, all while increasing brand loyalty.
Darwin Demo: Fraud Detection
This video shows how Darwin, an automated model building tool, empowers data scientists by using historical data sets to build a model that detects fraudulent transactions.
Darwin Demo: Predicting Insurance Pricing
This video shows how Darwin, an automated model building tool, empowers data scientists by using historical data sets to build a model that predicts insurance pricing.
Darwin Demo: Predicting Customer Churn
This demo shows how Darwin creates a machine learning model to predict customer churn.
Darwin Demo: Automated Loan Approval
This video shows how Darwin, an automated model building tool, empowers data scientists to build a model that lending institutions can use to understand their customer base and better price loans.
How to Maximize ROI From AI in Finance: Banking, Investing, and Insurance
Use Case: Predicting Financial Market Regimes with AI
The ability to more accurately predict market volatility, price changes, and price change directions comes with AI. To find the best possible methodology, one investment and trading company staged an
Use Case: Optimizing Energy Trading with Machine Learning
Utilities need to accurately forecast pricing for whole sales and retail markets to provide competitive offerings. Machine learning solutions allow utilities to move beyond the traditional approach.
Use Case: Alternative Data for Investment Management
From hedge fund managers to mutual funds and even private equity managers, alternative data has the power to improve valuation of securities and boost the clarity of the investment process.
Predicting Customer Insurance Costs with Artificial Intelligence
Predict customer insurance costs using machine learning. Darwin builds neuroevolution tailored models that reduce the amount of tie spent building and maintaining models. Darwin. Automated Model
Customer Success Story: KYC Process Automation for Banking
Why AI is Now Ready to Reach its Full Potential
Why AI is Now Ready to Reach its Full Potential.Now in its third wave, why is artificial intelligence only now truly disrupting the way we approach problem-solving? Usman Shuja of SparkCognition
White Paper: Extracting Value from Financial Documents
Financial institutions are saddled with huge amounts of documentation. According to Oracle, only 20% of all generated data is structured data, formatted to be easily understood by machines. The rest
TM17 - "AI in Finance" - Moiz Kohari