Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with the correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction, and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. Supervised learning is commonly used in applications where historical data predict likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.
Unsupervised learning is used against data that has no historical labels. The system is not told the “right answer.” The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering, and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.
Current State of Bond Selection
While the Internet helped us leap beyond reading a pile of print newspapers each morning, it has brought its own problems – namely many sources of news and alerts, filled mostly with duplicate stories and junk. In fact, only 0.5% of news stories are relevant to the credit risk of an issue. Dredging the Internet and other sources for information to inform risk assessments is time-intensive and ineffective. Still, most professionals in the muni bond industry scan the news each morning, looking for warning signs and make decisions on manually implementing changes to their bond portfolio using “guesswork and intuition”
Machine Learning Applications
Bond Intelligence uses several machine learning techniques to derive insight from patterns hidden in the data. Classification models have been created to aid in the decision-making process. The accuracy of our predictive models, provide decision makers the confidence when choosing the right underwriter. With pattern recognizing algorithms, our models can forecast future events with precision and can work as a powerful tool for recommendations involving numerical figures
Introducing Bond Robo Advisor®
Bond Robo Advisor® takes the manual work out of the monitoring, selection, recommendation, and implementation associated with issuing a bond. Bond Robo is an extremely performant, low latency and scalable solution which uses Big Data Machine Learning algorithms to collect client feedback, historical selection patterns, and recommends bonds based on our proprietary prediction methods and implements changes to the bond portfolios of the clients.
Bond Robo Advisor is the first Augmented Intelligence platform for decision making for Issuers. Our platform outperforms the competition with the most advanced Artificial Intelligence algorithm ever created with an easy to use, intuitive interface. Our platform merges Human, Collective and Artificial Intelligence in a self-service interface.