Practical Examples of Data Mining and Predictive Analytics in Finance and Healthcare

In today’s digital world, data is omnipresent. With your fingers at a keyboard or the screen of your phone, you have access to a bulk load of data — you just need to name the subject of your interest. The question is whether all this data is useful or relevant to you?

Businesses across industries rely heavily on this data and it can make or break deals depending on whether you are equipped with the right set of data or not. Precise and relevant data can give your business the right amount of fuel to not just run the long way but also lead from the front. On the other hand, faulty or disparate data can spell disaster.

Data mining and predictive analytics are complementary technologies that come under the umbrella of business analytics, and are leveraged by enterprises across industries to stay on top of the data war. While data mining helps you dig out, uncover patterns, filter, classify and analyze historical data that helps you transform raw data into actionable insights, predictive analytics leverages these results to forecast future outcomes. A combination of the two enable organizations to improve decision-making, optimize operations, and gain competitive advantages across industries such as retail, finance, healthcare, manufacturing, and more.

Let us take a look at how these technologies can help businesses in the sectors of finance and healthcare.

Data Mining and Predictive Analytics in Healthcare

All records of patients like medical history, tests taken, x-rays, scan reports, prescriptions, past treatments and outcomes, and many more are the data constituting healthcare data when it comes to an individual. Then there’s hospital or dispensary-related information like number of beds, equipment available, lab inventory, pharmacy stocks, operation theatre requirements, and also records of physicians, and other healthcare and admin staff.

Sounds exhaustive, right?

In fact, according to a report by RBC Dominion Securities Inc., “Today, approximately 30% of the world’s data volume is being generated by the healthcare industry. By 2025, the compound annual growth rate of data for healthcare will reach 36%. That’s 6% faster than manufacturing, 10% faster than financial services, and 11% faster than media & entertainment.”

Now, data mining in healthcare means sifting through enormous datasets to extract patterns, and valuable and relevant information. Data mining in healthcare makes use of various techniques like statistical analysis, natural language processing (NLP), and machine learning (ML) algorithms to arrive at meaningful insights. Also, predictive analytics is used to analyze this data and forecast future events.

Examples of Data Mining and Predictive Analytics in Healthcare

• Improved diagnostic accuracy: Data mining helps clinicians reach more conclusive and evidence-based diagnoses in a shorter span of time, after reviewing a patient’s past medical records, and current symptoms and lab reports. Gone are the days when a by-stander or the patient himself needs to run between laboratories and diagnostic centres and making multiple visits to the doctor. The concerned doctor can access all these on the screen of his tablet or laptop as all these records get digitized and automated, and gets sorted and classified with the help of data mining. Even though it would take an experienced doctor to make the final decision, the speed and accuracy of such AI-generated interpretations can make all the difference when treating complex conditions with indistinct symptoms.

• Detecting allergies and drug intolerance: It is a known fact that the same drug may not be suitable with all patients with the same symptoms. Because of various reasons, like lifestyle habits, genetic or others, different individuals can develop allergy or resistance to certain drugs. Also, certain dietary restrictions need to be followed while taking certain drugs. Even though the most common drug-allergic reactions and food-drug interactions are well documented, pharmaceutical science is ever-evolving and new drugs are introduced every now and then and there’s always a chance of human error. Hence, using machine learning for drug intolerance of patients, food habit and allergies etc. go a long way when it comes to administering treatment. Data mining can help present all such relevant data about a patient to consulting physicians.

• Enhanced clinical decision support: Besides analyzing individual patient records, data mining can also provide doctors with comparative analysis of diagnoses, medicines prescribed, treatment methods followed, and outcome of patients with the same or similar condition to the one being treated. This would give the clinician insight into the potential risks involved, right treatment procedures and more, thereby enabling them to make more evidence-based decisions.

• Better customer relationships: Integrating data mining into customer relationship management (CRM) software has many advantages. With the right data for analysis, hospitals can connect patients with certain conditions to the right experts, predict complications and recovery timelines and schedule follow-ups, track pharmacy purchases and more. All this would enhance the customer relationships.

• Insurance fraud detection: By analyzing the historical claims record of a patient, data mining and advanced analytics can detect false and forged claims or other inconsistencies and red flags in documents submitted by individuals.

• Predictive analytics: Predictive analytics makes use of data processed by data mining to forecast future outcomes. By employing predictive analytics, healthcare organizations would be able to prepare for spikes in seasonal and other infections, avoid staff shortage and drug understocking, manage in-patient and out-patient admissions, and more.

Data Mining and Predictive Analytics in Finance

Predictive analytics makes use of big data mining, statistics, modeling, machine learning, and artificial intelligence to analyze large volumes of data and make predictions about future outcomes or behaviors. By employing techniques such as pattern analysis, regression, and other statistical methods, predictive analytics helps financial institutions, insurance companies, banks, and investment firms detect patterns and trends in historical data to forecast cash flows, manage risks, and make informed investment decisions.

Examples of Data Mining and Predictive Analytics in Finance

• Forecast market movements: Predictive analytics uses the power of AI to predict future outcomes by analyzing historical data and comparing it with current market conditions and other variables. This helps investment firms to make data-driven decisions, manage risks and improve cash flow. It also helps them in creating future-proof marketing strategies and making fool-proof investment decisions.

• Credit risk analysis: No matter whether we are rich or poor, a good proportion of us rely on loans or credit cards to fulfil one or the other life’s requirements, be it for household shopping, buying a house or vehicle, or for business purpose. Traditionally, getting a loan or a credit card required numerous visits to the bank, submitting a number of documents, getting approvals etc. Introduction of data mining and predictive analytics into the banking sector has helped to streamline much of this tedious process by means of digitizing and automating them. Also, by analyzing your current financial commitments, past loan or credit cards repayment history, patterns of payments made and various other factors, banks and other financial institutions can determine your creditworthiness, whether you are eligible for a loan or not, and even the maximum amount you are eligible for.

• Fraud detection and prevention: With the help of advanced analytics and machine learning algorithms of data mining, professionals can analyze transaction patterns and detect anomalies in real-time. This is useful in recognizing deviations from normal behaviour and take proactive measures to minimize financial losses. It also helps in protecting customers’ trust by ensuring that every transaction is made transparent and scrutinized with unmatched precision.

• Insurance underwriting and claims analysis: Insurance professionals can make use of data mining and predictive analytics to profile customers, their transaction patterns, social media activities and more to foster more accurate underwriting practice and make better risk assessment. Predictive analytics in insurance claims and underwriting can further examine a multitude of variables to streamline the claims process and identify anomalies indicating potential frauds, thereby enhancing the claims management process. This would not only prevent financial losses but also help build trust and loyalty among policy holders.

• Enhance regulatory compliance and reporting: The role of big data in improving regulatory compliance cannot be understated. Financial institutions are bound by strict industry regulations and standards. Data mining helps professionals to decode the nuances of regulatory requirements and have a better understanding of them so that they can be implemented without compliance errors. Also, automation and continuous monitoring and analysis of vast datasets makes sifting through them easier in real-time and identifying patterns, anomalies and deviations that might indicate non-compliance. These technologies also help financial institutions to swiftly adapt to changes in regulations, and also in maintaining audit trails and documentation.

• Customer churn and retention: By analyzing current and historical transactions made by an individual using data mining, financial institutions can better understand customer engagement patterns and identify churn indicators and attrition factors. Customer churn prediction using financial transaction data can help organizations to provide tailor-made offerings to those customers, especially high-profile ones, thereby incentivising them to continue with them. Customer retention is as important as acquiring new ones for the profitability and credibility of a financial institution.

Conclusion

The above mentioned are but some of the advantages that data mining and predictive analytics offer to the healthcare and financial sectors. All these business analytics techniques contribute to reduction in cost, and human intervention needed in different processes, thereby improving efficiency, productivity and profitability of these institutions.

Nevertheless, to achieve this you would need the expert guidance of professionals who have hands-on experience in implementing technologies like data mining and predictive analytics and drawing meaningful results from them. This is where DeepKnit AI can come to your rescue. Consult with our experts to know more.