Build supervised algorithms that predict future prices and liquidity movements, by extracting hidden features within data. Building flexible models that learn from new trends by adapting to the free flow market information. Determine assets that are mispriced as found in niche markets.
Improve the accuracy of risk modelling by identifying key data features and nonlinear patterns within large datasets. Enhance your decision making with well-trained deep learning models, that ensure a more efficient means to manage risk. Build early warning systems that automate reporting, portfolio monitoring, and contingency plans.
Construct well-diversified portfolios using unsupervised learning, such as cluster analysis to identify assets with similar characteristics, and reinforcement learning to allocate capital dynamically in an ever-changing environment. Use regularisation and semi-supervised learning for portfolios with label data more expensive to assemble.
Natural Language Processing (NLP)
Make use of NLP to more intricately assess value in investment strategies such as long/short equity and discretionary with models that more accurately gauge market sentiment. Provide highly relevant answers to questions related to financial planning.
Fraud Detection and Identity Management
Machine learning in fraud detection has become the go-to technology for classification of fraudulent behaviour. Supervised learning methods such as neural networks have shown to be very effective at detecting fraud when trained on a large amount of financial statement data.
Determine the credit-rating of a borrower from social media activities and transaction history. Build new credit markets with customer and clients that lack a long-term credit history. Use supervised learning to more accurately assess companies’ credit scores, e.g., using neural networks.
Build self-learning models that predict sales, help increase sales revenue, and reduce storage costs. Using Linear Latent Variable Models (LAVA) and/or Elastic Nets to estimate the latent factors that highlight customers purchasing behaviour.
Big Data Analytics and Visualisation
Systematic analysis of big data is crucial when exploring under-performing streams of sales revenue. By deploying a combination of large-scale analytics and data visualisation we can illuminate hidden campaign strategies, such as cross-sales, which will alleviate such poorly performing SKUs.
Implement statistical models with demand and supply uncertainty features that are inherent to the supply chain process. The perturbation of these model treat hidden externalities and generate a robust toolkit for modelling supply chain. Some additional areas where machine learning could help you’re your business is planning group problems, optimising stock levels, and warehouse automation.
Backtesting Campaign Strategies
Campaigns can be costly if they are not implemented correctly, and thoroughly backtested. Finely tuning tune back-testing models will help build a well-constructed cost-effective campaign strategies, giving management at all levels the details and implications for deployment.
Targeted Campaign and Retail Segmentation
Have a nuanced view of public opinion and target customers more accurately with Multi-level Regression and Poststratification (MRP). Create retail segmentation with artificial neural networks (ANNs) giving you a better understanding of your customers’ shopping habits.
Improve customer segments and targeted advertising using machine learning segmentation methods (e.g. cluster analysis, k-means, Nearest Neighbour). Classify customers using Supervised Learning Models, find new audiences using recommendation systems increase the efficiency of your media spend.
Find patterns in the way customers interact with your brand by using predictive modelling and forecasting. Optimise conversions, increase customer satisfaction.
Social Media – Early Opportunity Detection
Analyse real-time Twitter and Facebook data streams to capture current sentiments concerning brands, products or adverts. Get a head start on sentiment outbreaks to uncover important opportunities and avoid PR crises.
Sales and Marketing Integration
Build an easy to navigate interface to measure an integrated impact of sales teams and marketing campaigns. Track direct correlation between media budgets and number of products sold.
Improve efficiency of your campaigns using social network analysis. Find audience most susceptible to the message, and use this audience to amplify the impact of your campaigns. Find and target.
Implicit Survey Design
Improve accuracy of surveys by using established psychological tools and test instruments, such as gamification. Learn about your audience to make informed decisions.
Optimise website usability and impact using eye tracking methods too. Investigate consumer’s perception of adverts using brain imagining (fMRI) analysis.
Improve your customer service with specialized chat bots that interact with patients through chat windows. Automate scheduling follow-up appointments with patients. Minimise human error by ensuring they are directed to the appropriate healthcare department, and reduce kpi times.
Build state of the art classification algorithm for diagnosing patients based on mere mobile phone photos. Identify rare diseases with learning algorithms such as functional-gradient boosting (FGB), which self-report behavioural data to allow distinguishing between people with rare and more common chronic illnesses.
Supervised learning will allow physicians to select from more limited sets of diagnoses. An example of this is the estimation of patient risk factors relative to symptoms and genetic information. Such models can be calibrated and trained on micro biosensors and mobile phone applications which will give more sophisticated health data to assess treatment efficacy. Reduce treatment cost and optimize individual patent health.
Machine learning in early-stage drug discovery can be used to estimate the success rate of initial screening of drug compounds relative to biological factors. The application of unsupervised learning (k-nearest neighbor algorithm) to precision medicine has identified mechanisms in multi-factor diseases and created alternative treatments and therapies.
Clinical Trial Research
Selecting and identifying ideal candidates for clinical trails by sampling from a broader range of data to find features that are currently underutilised, an example of this could be social media and number of doctor visits. Use machine learning to improve the safety of the trialists by monitoring their health in real-time remotely.
Epidemic Outbreak Prediction
The monitoring and predicting of epidemic outbreaks have been performed successfully by machine learning technologies for a number of years now. Collecting vast amounts of data from satellites, historical healthcare databases, and social media; one can train support vector machines and deep neural networks potential outbreaks such as malaria and Ebola.