How To Makes Use Of Domain Knowledge In Data Science: Examples From Finance And Health Care


    The domains of finance and health care don’t have much in common except for one thing — the involvement of data scientists and machine learning experts, who are changing the way both these domains work. From helping them collect, organize, and process a massive volume of data and making sense of it to letting them make efficient and faster data-driven decisions, a lot is happening to disrupt both these domains. Let’s consider some examples from both the finance and healthcare sectors to understand how the application of data science or domain knowledge in data science is helping them.

    Domain Knowledge In Data Science



    1. Financial Risk Management and Risk Analysis

    For a company, there’re different financial risk factors like credits, market volatility, competitors, etc. For financial risk management, the first step is to identify the threat, followed by monitoring and prioritizing the risk. Several companies depend on data scientists to analyze their customers’ creditworthiness. This is done with the use of machine learning algorithms to evaluate the customers’ transactions. Again, if the risk of a finance company is associated with stock prices and sales volume, time series analysis where variables are usually plotted against time could be helpful.

    2. Financial Fraud Detection

    By analyzing big data with the use of analytical tools, financial institutions can detect anomalies or unusual trading patterns and receive real-time detection alerts to investigate such instances further. This would help in keeping track of frauds and scams.

    3. Predictive Analytics

    For a finance company, predictive analytics are crucial as they disclose data patterns to foresee future events that can be acted upon right now. Data science can use sophisticated analytics and help in making predictions based on data from news trends, social media, and other data sources. Thus, with predictive analytics, a finance company can predict prices, future life events, customers’ LTV (lifetime value), stock market moves, and much more, all of which will let it decide and strategize the best way to intervene.

    4. Personalized Services

    NLP (natural language processing), machine learning, and speech recognition-based software can analyze customer information and produce insights about their interactions. For instance, an AI-powered solution can process an individual’s basic information that he has specified in a questionnaire in addition to gathering data about his online behavior on a financial company’s website, his historical transactions, and his feedback, likes, comments, etc. on the company’s social media pages. All these would help the company optimize and customize its offerings to serve the individual (i.e. the customer) better.



    1. Medical Image Analysis

    With the use of machine learning and deep learning algorithms, image recognition with SVMs (Support Vector Machines), and MapReduce in Hadoop, to name a few, it has become possible to find microscopic deformities in medical images and even enhance or reconstruct such images.

    2. Genomics

    By using advanced data science tools like SQL, Bioconductor, MapReduce, Galaxy, etc., it has now become possible to examine and derive insights from the human gene much more quickly and in a more cost-effective way.

    3. Predictive Analytics

    A predictive model in health care uses historical data to learn from it and discover patterns to produce accurate predictions. Thus, with data science, you can find correlations between diseases, habits, and symptoms to improve patient care and disease management. Predictions of a patient’s health deterioration can also help in taking timely preventive measures, while predictions about a demand surge can facilitate adequate medical supply to healthcare facilities.


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