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Data science and AI are impacting various sectors including finance and healthcare.
Data science and AI are impacting various sectors including finance and healthcare.
In the financial services sector, data is the most important resource and integrating machine learning techniques is a necessity to extract insightful intelligence.
Fraud detection, automating risk management, real-time consumer analytics, algorithmic trading, and deep personalization of customer relationships are among the many use cases that AI can help the financial industry.
With the vast amount of data available in the healthcare sector such as clinical, financial, R&D, and administration and operational, data science can derive meaningful insights to improve the outcome and efficiency of the industry.
Predictive big data analytics can help in cutting down healthcare costs, providing better care, improving administrative performance, and reducing readmission. Please see our white paper on using predictive analytics related to lung cancer readmission.
Artificial Intelligence (AI) is emulating human intelligence and process by software that combine learning, predicting and self-correcting patterns. This involves using three key components of machine learning, deep learning, and natural language processing.
AI has been a main contributor in many organizations and its applications cover a wide range such as email categorization, process automation, security surveillance, anomaly detection, and smart devices or assistants.
We identify use cases that focus on your business strategy. This means defining unique AI priorities that benefit you the most. Our AI solution architect team starts by understanding your organization goals, challenges you are facing, and the key results you expect.
We further dive deep into approach, planning, data needed, required KPIs, ethical, and legal matters.
Machine learning can be summarized as a branch of AI that uses statistical and AI powered modelling to learn from experience, identify patterns, and improve decision making or prediction accuracy. We train algorithms that optimize your specific regression or classification metrics. More data does not necessarily mean better results, and sometimes so many features can introduce correlation into the mainstream algorithms. The Allwyn data science team can use the latest exploratory data and feature analysis techniques that enable feature and model selection. We analyze combination of different features and use latest data engineering techniques to find hidden value inside your data.
Based on the use case of the model and whether the model needs highly informative interpretation we provide white or black box models. From basic regression models to advanced neural networks we provide a wide variety of solution that maximizes training efficiency, optimizes hyper parameters and fine tunes the target scoring metric while aiming for the least error that prepares the model for the production ready frameworks.
Many ML algorithms tend to fail when the training data is imbalanced and they end up generating inaccurate biased results. We use the latest techniques in data balancing to optimize your data for machine learning algorithms. Our data science experts can create similar synthetic representations of your data points in cases where the target label have less than the optimum balance.
Additionally, our data transformation pipelines can apply ML algorithms to each data balancing method to discover which would be a suitable predictor.
Whether you need experts in Python, R, Spark, or other programming languages, our data science team is flexible to switch and adapt to your technology needs.
We can create pipelines and ML programs that use a combination of different methods and programming languages to so you’ll have the least change in your data and technology structure.
