Data analytics has proved to be an invaluable tool for improving security, preventing fraud, and increasing operational efficiency in the BFSI sector by analyzing raw data to uncover trends and insights.

We will examine the top 6 data analytics use cases in this article that are propelling the BFSI sector’s digital transformation.

1. Fraud Detection and Prevention Using Data Analytics in BFSI

Fraud is a constant threat in the quick-paced world of financial services and can cost banks, insurance companies, and other financial institutions a lot of money. It should come as no surprise that cybercriminals frequently target the BFSI sector, searching for vulnerability to exploit, given the amount of money at risk.

BFSI institutions can, however, turn the tables on these fraudsters thanks to the power of data analytics. Financial institutions can identify and stop fraud before it even starts by utilizing advanced analytics techniques like predictive modelling, machine learning, and data mining.

The secret to success is searching through the massive amounts of data produced by BFSI institutions for patterns and behaviors that could point to fraudulent activity. Financial institutions can identify potentially fraudulent activities and act before they cause significant financial harm by developing predictive models based on historical data.

Data analytics is a way to stay ahead of the competition as well as a tool for preventing fraud. BFSI institutions can spot new opportunities and maintain a competitive edge by utilizing the insights gained from data analytics.

BFSI institutions can protect their customers’ funds and open up new doors for growth and success by using the right analytics techniques and a commitment to constant vigilance.

Few Examples

To improve fraud detection and prevention, the BFSI sector can use data analytics in a number of ways. These strategies include, as some examples:

Money laundering

Fraudulent activity may involve moving money through multiple accounts to conceal the source of money that was obtained illegally. Using data analytics tools to identify anomalous patterns in transactional data, which can then be reported to the bank, it is possible to identify potential money laundering activities. While an investigation is being conducted, the bank may take the necessary action, such as alerting the appropriate parties or freezing the affected accounts.

Insurance Fraud

Making a false claim for financial gain constitutes filing a fraudulent insurance claim. Data analytics tools can be used to examine claims data and look for patterns and discrepancies with legitimate claims to find these fraudulent activities. Insurance companies are able to identify and stop the payment of fraudulent claims in this way.

False Credit Card Transactions

Data analytics solutions can identify possible fraudulent activities by examining credit card transaction data, including purchase history, transaction amounts, and location information. This enables banks to recognize such transactions and stop them from being approved, ultimately preventing fraud.

2. Personalized Customer Experience Through Data Analytics in BFSI

Ingenious business models that personalize customer journeys and advance financial inclusion have been developed by BFSI organizations thanks to the advanced capabilities of data analytics technologies like machine learning and Artificial Intelligence (AI). With the help of data analytics, BFSI institutions can use bots to communicate with customers in a variety of languages and dialects, offering individualized and practical branch-like services.

Furthermore, based on customer activity, big data and AI-driven data analytics can analyze customer profiles, behaviors, and needs, enabling institutions to suggest suitable financial services and products. Data analytics solutions have sophisticated natural language processing and machine learning capabilities that allow for accurate understanding of customer intent, facilitating contextual engagement and raising customer satisfaction.

Customer data analytics, for instance, can enable chatbots and voice assistants to give customers wise investment and savings advice. AI-enabled voice assistants can also assess a customer’s loan eligibility, facilitate disbursement, and keep track of equated monthly installments thanks to data analytics (EMIs).

Also Read: Testing a bank application: A Success Story

3. Risk Management Through Data Analytics in BFSI

The BFSI industry is exposed to a variety of risks, including credit, operational, regulatory, liquidity, and market risks, all of which have the potential to endanger their operations. BFSI institutions use data analytics tools to effectively identify and manage these risks to reduce their impact.

Businesses in the financial services industry (BFSI) can learn more about various facets of their operations and spot potential risks by analyzing data. These insights can be used to evaluate risks and create individual mitigation plans for each one. Data analytics, for instance, can be used to analyze customer behavior, spot fraud, keep an eye on market trends, and assess the creditworthiness of customers. Due to their ability to manage risks in real time and make informed decisions, BFSI companies can avert potential problems before they become serious.

4. Predictive Analytics for Investment Decisions in BFSI

Predictive analytics is an essential tool for BFSI companies to use when making informed investment decisions. BFSI companies are constantly looking for investment prospects. These businesses can analyze historical data and statistical models to gain insights into upcoming market trends by utilizing predictive analytics, allowing them to recognize and seize potential investment opportunities.

Here are a few ways that BFSI uses predictive analytics to make investment decisions.

Portfolio management and assessment

Using historical data, predictive analytics can assess the returns & risks related to a specific investment. The predictive analytics model can assist BFSI firms in identifying trends and patterns that may indicate an investment’s likelihood of success or failure, enabling them to decide whether to pursue the investment opportunity or not.

Financial advisor assessment

Firms can analyze the performance of an advisor (internal and independent) by using data analytic techniques. It is possible to predict which advisor is expected to bring in higher revenues by assessing their past performance. Firms can in turn keep these set of advisors highly motivated thus enabling them to beat their past performance and generate higher revenue.

Customer Segmentation

To classify customers based on their investment preferences and behavior, BFSI companies use predictive analytics. Predictive analytics models can identify patterns and trends in customer data through customer analysis, allowing BFSI companies to tailor their investment products to the specific requirements of various customer segments.

5. Regulatory Compliance Through Data Analytics in BFSI

BFSI institutions operate in an environment that is highly regulated, and failure to comply with regulatory requirements can result in costly fines, negative legal consequences, and a damaged reputation.

As a result, these companies must devise creative strategies to guarantee that all legal requirements are met. One such solution that can assist BFSI companies in complying with regulations is data analytics.

Here are some strategies for using data analytics to help BFSI firms comply with regulations:

Reporting

BFSI organizations, as previously mentioned, heavily rely on data. However, it can be difficult to manually generate reports that show compliance with regulatory requirements. In situations like these, data analytics is essential. BFSI companies can use data analytics tools to analyze all data pertaining to compliance activities and produce reports that show regulatory bodies that the company complies with its compliance obligations.

Monitoring Compliance

By examining vast amounts of data related to compliance, BFSI companies can use data analytics to track their adherence to regulatory requirements. This makes it possible for them to spot potential compliance problems and take appropriate action to stop them from developing into serious issues.

Audit Management

By giving auditors the knowledge, they need to assess compliance and pinpoint areas for improvement, BFSI companies can use data analytics to support the auditing process. This reduces the possibility of regulatory fines and helps organizations avoid costly compliance mistakes. Data analytics can offer insights that help auditors in their evaluation process by analyzing data pertaining to compliance activities, such as identifying potential risks and areas of non-compliance. BFSI companies can avoid compliance problems and guarantee that they are successfully adhering to regulatory requirements by doing this.

Read our Success Story on : Real-time collaborative Fraud Analytic solution to combat Identity Theft

6. Cybersecurity Using Data Analytics in BFSI

Cyberattacks and fraud are very common in this sector. An isolated security lapse can result in sizable monetary losses and harm to a company’s reputation. Because of this, data analytics are essential to identifying and preventing cyber threats. Cybersecurity is therefore of the utmost importance in this industry. The BFSI industry can promote cybersecurity through data analytics in the following ways.

Threat Detection

Data analytics can identify potential cyber threats by examining trends and patterns in network traffic or other data sources within BFSI systems. The BFSI company can take appropriate action to eliminate the threat as soon as an abnormal activity is discovered, preventing further damage.

Incident Response

Data analytics helps incident response by supplying real-time data and statistics on cyber threats and security incidents. This allows BFSI organizations to quickly respond to potential security incidents in order to stop them from escalating.

Risk Assessment

Data analytics can be used by BFSI companies to assess the risks of cyberattacks. They can identify areas of risk and create risk mitigation strategies to protect their data from unauthorized access by analyzing data on potential vulnerabilities and cyber threats.

Compliance Management

To make sure that BFSI companies adhere to the various cybersecurity standards and regulations governing their operations, data analytics tools can be used. With the aid of these tools, the company can identify compliance gaps in cybersecurity-related activities and take the necessary corrective action to be following legal requirements and industry best practices.

Wrapping Up

The BFSI sector has always relied heavily on data, but data analytics is pushing that dependence to new heights. BFSI companies can use data analytics to drive digital transformation and open new opportunities for growth by leveraging their data.

BFSI companies can reduce fraudulent activity, personalize customer experiences, increase operational effectiveness, and guarantee regulatory compliance by using data analytics. Furthermore, data analytics can aid in the detection and prevention of cyberthreats, protecting sensitive data from unauthorized access.

BFSI businesses must embrace digital transformation and use data analytics tools in order to stay ahead of the competition. They can accomplish operational excellence by doing this, giving them a competitive advantage in the market.

Our team is here to support BFSI organizations integrate data analytics into their processes as they lead the way in digital transformation. Get in touch with us right away to find out more about how we can support your digital transformation efforts by assisting you in maximizing the power of data analytics. Click here for more details…

The post How Data Analytics Is Transforming the BFSI Sector appeared first on Indium.

Some of the benefits of MLOps include faster and easier deployment of ML models. It helps with continuous improvement in a cost, time, and resource-efficient way by facilitating collaboration between different teams and tasks. The models can also be easily reused for other use cases using MLOps. As validation and reporting are an integral part of the system, it makes monitoring easy.

Preparing to Build the MLOps Architecture

The development of an MLOps project is just like any other project, but with a few additional steps to ensure an easy and seamless flow.

● Setting up the Team: Planning and assembling the right team is the first step. Depending on how complex the project is, the team will include one or more ML engineers, data engineers to manipulate data from various sources, data scientists for data modeling, and DevOps engineers for development and testing.

● ETL: For data modeling to develop machine learning algorithms, data needs to be extracted from various sources, and a pipeline created for seamless data extraction in the system. The data needs to be cleaned and processed using an automated system that helps with seamless transformations and delivery.

● Version Control: Like in DevOps, version control plays an important role in MLOps too, and Git repository can be used for this as well.

● Model Validation: In DevOps, testing is important and includes unit testing, performance, functionality, integration testing, and so on. The equivalent in an ML project is a two-step process – model validation and data validation.

● Monitoring: Once the software has gone live, the role of DevOps ends until the time of further enhancement. In an MLOps project, though, periodical monitoring of ML model performance is essential. This is done to validate it against the live data using the original validation parameters. This will help identify any problems, and the modeling will have to be reworked

Must read: MLOps on AWS: Enabling faster

Databricks for MLOps Architecture: 5 Things to Consider

While this makes MLOps sound ideal and easy, in reality, one of the challenges it faces is the need for a huge infrastructure, including computing power and memory capacity that on-premise systems cannot meet without additional costs. Therefore, cloud architecture is a better alternative that allows for quick scaling up and down based on need and thereby keeps costs based on need.

It also needs constant monitoring due to the ever-changing requirements and the need for the models to reflect these changes. As a result, businesses must frequently monitor the parameters and modify the variables of the model as and when required.

Some key challenges may also arise in MLOps with regard to managing data, code, and models across the development lifecycle. Multiple teams handling the various stages of development, testing, and production collaborate on a single platform, leading to complex needs for access control and parallel use of multiple technologies.

Databricks, with its robust ELT, data science, and machine learning features, is well-suited for building the MLOps architecture. Some of the factors that make Databricks consulting services ideal for MLOps include:

● Lakehouse Architecture: Databricks uses a Lakehouse architecture to meet these challenges and unify data lakes and data warehouse capabilities under a single architecture and use open formats and APIs to power data workloads.

● Operational Processes: The process of moving the ML project through the development cycle should be clearly defined, covering coding, data, and models. Databricks allows the code for ML pipelines to be managed using the current DevOps tooling and CI/CD processes. It simplifies operations by following the same deployment process as model training code for computing features, inference, and so on. MLflow Model Registry, a designated service, is used to update code and models independently, enabling the adaption of DevOps methods for ML.

● Collaboration and Management: Databricks provides a unified platform on a shared lakehouse data layer. In addition to facilitating MLOps, it allows ML data management for other data pipelines. Permission-based access control across the execution environments, data, code, and models simplifies management and ensures the right levels of access to the right teams.

● Integration and Customization: Databricks used open formats and APIs, including

– Git

– Related CI/CD tools

– Delta Lake and the Lakehouse architecture

– MLflow

Additionally, the data, code, and models are stored in open formats in the cloud account and supported by services with open APIs. All modules can be integrated with the current infrastructure and customized by fully implementing the architecture within Databricks.

● Managing the MLOPs Workflow: Databricks provides developers with a development environment to allow data scientists to build the ML pipelines code spanning computation, inference, model training, monitoring, and more. In the staging environment, these codes are tested and finally deployed in the production environment.

Check out our MLOps solution Capabilities

Indium’s Approach

Indium Software has deep expertise in Databricks and is recognized by ISG as a strong contender for data engineering projects. Our team of experts works closely with our partners to build the right MLOps architecture using Databricks Lakehouse to transform their business. Ibrix is an Indium Unified Data Analytics platform that integrates the strengths of Databricks with the capabilities of Indium to improve business agility and performance by providing deep insights for a variety of use cases.

Inquire Now! To know more about how Indium can help you build the right MLOps architecture using Databricks solutions.

The post Building the Right Architecture for MLOps appeared first on Indium.

With the booming number of users Data privacy is at its stake. Content creators of social media are a lot but are they equipped enough with data security? Managing the privacy of customer data is one of the biggest challenges faced in social media, according to a report by Hoot suite Social Trends Survey 2022.

As per Gartner’s report, 75% of the world’s population will have its personal data covered under modern privacy regulations, by end of 2024. This regulatory component is the critical catalyst for the operationalization of privacy.

With the privacy regulations across the globe, organizations should focus on privacy enhancing computation techniques to meet the challenges of protecting the data.

Increasing complexity of analytics engines and architectures mandates that social media owners incorporate a by-design privacy capability. AI models and techniques helps in designing this. Unlike common data-at-rest safety measures, privacy enhancing computation protects data in use.

While incorporating the algorithm is one side of the spectrum, validating the data logic plays a crucial role to ensure that the desired outcome is achieved.

Working with one of the top social media creators, we have created a checklist to help validating the data privacy augmentation.

Here is a glimpse of the control points for which we have a checklist created for validation:

• Web / DNS control points
• Email control points
• Executable control points
• Content control points

We have created a 3 stage model to achieve this.

Setup 

The Setupphase includes foundational capabilities of a privacy management. Identifying control points, defining business rules, record keeping. These are needed for any customer-facing organization that processes personal information. These include discovery and enrichment to establish and maintain privacy risk registers

Maintain

The maintain phase is to scale and focus on ongoing administration and resource management. Categorization of the controls and actions on the incidents observed, thereby bringing automaton to privacy impact considerations. Tools are used to identify the contents and block based on the rules.

Progress

The progress phase is updating the rules based on the controls and mitigating the risks identified. Its more of a continuous improvement phase.

Now lets move on the checklist for validation.

• Web / DNS control points

This is more of filtering on the unauthorized access from DNS / web addresses. List of control points used in this validation with multifold techniques. Repository of unauthorized DNS addresses created with responsive actions. DNS control points blocks content or network access from potentially harmful sources. Control point will have a block list or allowlist to filter harmful unwanted content. API will identify the blocked domains on the CMS and restricts user to create account, add email using those domains.

• Email control points

Screening of emails for spam and objectionable content forms this email control points repository created with rules of actions. API will identify and restrict banned users from login to the application.

• Executable control points

Few files while opening or executing triggers unwanted actions and those are added in executable control points. Rules are programmed to stop execution of the malicious files.

• Content control points

Content based controls use pre moderation and post moderation techniques to filter and bypass the unwanted content. API identifies the word configured in CMS and restricts user from posting those words.

Pre-Moderation Server will identify and restrict users to create account using configured words, will identify configured words on posts, comments and quote posts and will block users from posting. Post-Moderation Server will identify and delete words/images/video which meets the AI score

These controls are configured to exclude undesirable unwanted type of content that violates the product’s acceptable use policies.

Above checklists helps to validate the data privacy augmentation rules and protects the organizations and individuals against harmful content.

Validation helps to minimize unwanted access and content thereby making the social media safe for the ever increasing users.

The post Checklist for Validation of Data Privacy Augmentation Computation in Social Media appeared first on Indium.

It is referred to as text mining. One of the prominent reasons owing to which people use it is for the extraction of additional data from the unstructured data sources with an eye to enriching the master data of the customers with an eye to production the new customer insight.

It is also useful for the determination of sentiments and different types of products and services.

Results of the survey, tweets, online reviews, emails and different kinds of written feedback consist of insight into the customers.

The recorded interactions have a bunch of information that can be transformed into the text without any hassles.

According to Markets and Markets, the global text analytics market value is forecast to reach USD 8.8 billion by 2022 with a compound annual growth rate (CAGR) of 17.2 percent. Social media analytics and the growing need for predictive analytics for businesses are among the major factors driving the growth of the text analytics market.

With the aid of text analytics, you will be capable of uncovering a wide array of themes and patterns.

Thus, you will have an information about the thoughts of your customers. With it, you will be able to gain an understanding of their requirements and needs.

Top Tools for Text analytics

Text analytics with Hadoop

Analyzing text with the aid of Hadoop happens to be an amazing option when the full volume of the source files is huge and the Hadoop Cluster has prerequisite sources.

Thus, the analysis of the text happens more quickly in Hadoop. The text analysis is known for the extraction of entities from the unstructured text.

It is helpful for the transformation of the unstructured data into the structured data. This is crucial for running any sort of analysis with the aid of the data in text resources.

You just require the text sources as the input for the analysis of the text.

Later on, you can remove the text sources theoretically, as they will not be required for the process of analysis.

Text analytics with HANA

With the aid of SAP HANA, it is possible to extract real insight from the unstructured data.

This platform stands out of the ordinary in offering text analysis, search and text mining functionality from the unstructured text sources.

Statistical algorithms can be applied by which you can detect the patterns in the large document collections, which is inclusive of key term identification as well as document.

Almost 80 percent of the relevant information of the enterprise is derived from the unstructured data.

With the aid of SAP HANA, you can get access to the greater volume of data that is inclusive of unstructured text data from a wide array of sources. SAP HANA allows people to do the full-text analysis.

Text analytics with R

Here is the list of the leading four options that are used in the Big Data Services industry with an eye to accomplishing text analysis in R:

Keyword Match Algorithm

It is considered to be the most powerful tool for performing text analysis. It stands second to none in the extraction of keywords from the not so well separated keywords.

It comes with the option to assign priority to the algorithm. You, however, require a pre-defined list of keywords from where you require searching.

At times, it has been seen to capture a few types of misclassified cases.

Word match algorithm

This is known to be the fix for the min-classified cases that are found in the last algorithm.

Here, words are matched in lieu of the keywords. It functions in a perfect manner to find the well-separated words.

For example, with the aid of this algorithm, it is possible to extract the word Ramesh from Ramesh Shastri.

It enables the priority order as well. For example, in case you intend to give higher priority to Ramesh than Shastri in the above-mentioned tag, it can be executed at ease.

General Expressions

This process requires extensive research from the sentence structures. In order to begin with it, you do not require any sort of list.

The percentage of accuracy is really high if you gain success in finding the stronger and regular expression.

An in-depth research is required for the creation of regular expression. In case the data is not structured well, this process lets you tag a smaller number of the observations.

This algorithm can be used if you are not aware of the language of the text. It functions as the feedback to the other algorithm.

If parameters are optimized in a perfect manner, it can be predicted with ease. You do not need any dictionary.

It is also used for providing feedback to the other algorithms. At times, it is not that precise in the name of the subject.

It has a tendency for capturing the trends that do not indicate anything significant.

Text analytics with Excel

Excel is recognized to be an effective and convenient solution for accomplishing your requirements for text analysis.

You can go for an analysis of several customer reviews for gaining an insight into the product.

The Excel add-in functions on ParallelDots AI APIs that are used by the enterprises and developers for empowering the analytics for the past two years.

You can conduct keyword analysis on a bunch of negative and positive sentences with an eye to understanding why people are disliking or liking the product.

This analysis let you get an insight into the key phrases that contribute to the sentiment about the product.

For instance, a phone manufacturer can conduct the analysis of reviews from the social media, eCommerce sites, and tech review blogs.

After that, keywords can be extracted for the negative and positive sentiment sentences for finding the features, disliked or liked by the users regarding about a specific phone model.

In addition to this, you can go to a higher level by the analysis of the product reviews and then categorize the same with an eye to identifying if the review is a query, feedback, spam or opinion.

This is useful to filter the essential reviews and then act on them quickly.

You can correlate the analyzed data such as intent, keywords, and sentiment with the internal business metrics like sales data, marketing spends for getting actionable insights.

Text analytics with Python

Here are some of the applications in which python stays at the forefront that enable the use of a wide assortment of advanced libraries, specifically the natural language processing toolkit.

It comprises of a series of libraries and advanced functions for the performance of specific operation present in the text for pre-processing it for using the same for the derivation of information from the same.

Chatbots

Though customer service is present for most of the products, it is not available always effective as most of the people want their complaints should be solved during the usual working hours, thereby resulting in a rush. Chatbots are considered the perfect solution to the same.

Sentimental analysis

During online shopping, most of the customers provide feedback. This feedback is classified into the categories like negative, positive and neutral, thereby letting the customers make a better-purchased decision about the product.

It also helps the company in filtering out the flaws from the negative reviews for the improvement of the product.

Conclusion

The text analytics is known for conferring the early warning of the trouble as it showcases the points, your clients are not satisfied with.

With the aid of the text analytics tool, you will gain success in extracting valuable details from the data that cannot be quantified in the other ways at ease.

It is useful in turning the unstructured thoughts of customers into the unstructured data at ease that you can use for your business.

The post 5 Widely used Tools and Techniques for Text Analytics appeared first on Indium.

How are healthcare organizations leveraging predictive analytics to derive actionable insights from their ever-growing datasets? We find out here.

Staying ahead of Patient Health Deterioration

It is the most essential application of predictive analytics in healthcare.

It helps caregivers react quickly to any change in a patient’s vitals and gather foresight into possible deterioration before symptoms are evident.

A 2017 study demonstrates this: at the University of Pennsylvania, a predictive analytics tool using machine learning and EHR data helped identify patients vulnerable to severe sepsis or septic shock a full 12 hours before the onset of the illness.

Predictive insights are particularly valuable in the intensive care unit (ICU), where timely intervention can help save someone’s life and prevent patient health deterioration.

The increased adoption of wearable biosensors offers manifold benefits, too, for care providers. They enable remote health monitoring and help detect early symptoms of health deterioration.

Preventing Patient self-harm

Early identification of individuals likely to self-harm will help provide the essential mental healthcare to avoid potentially serious or fatal events.

According to the World Health Organization, almost 800,000 people die of suicide each year, which is one person every 40 seconds.

Studies have showed that predictive analytics, using electronic health record (EHR) data and depression questionnaire, helps identify individuals at higher risk of committing suicides or other forms of self-harm.

In a study led by Kaiser Permanente (a leading American healthcare provider) and conducted together with Mental Health Research Network, EHR data combined with a depression questionnaire helped accurately detect those with a higher risk of attempting suicide.

Another study, featured on the American Journal of Psychiatry, aimed to build and validate predictive models with the help of electronic health records to predict suicide attempts and suicide deaths after an outpatient visit.

Based on predictors such as prior suicide attempts, mental health substance diagnoses, mental health and more, it was found that within 90 days of a mental health visit, suicide attempts and suicide deaths among individuals in the upper one percent of predicted risk were 200 times more common than those in the bottom half of the predicted risk scale.

Predicting patterns in patient utilization

Predictive analytics helps healthcare organizations ensure adequate staffing levels for busier clinic hours, minimize wait times and improve patient satisfaction.

With the help of big data visualization tools and analytics strategies to model patient flow patterns, healthcare centers can ensure the inpatient department has adequate beds available for patient admission, that the outpatient and physician offices have enough resources to reduce patient wait times and manage workflow and scheduling adjustments accordingly.

Scheduling changes help nurses and doctors cope with the increased patient flow while reducing the burden on them, thus ensuring they provide timely care and improve patient satisfaction.

Data Security

Predictive analytics and artificial intelligence (AI) play a key role in boosting cybersecurity, with the sophistication of cyberattacks (involving malware, phishing and more) rapidly on the rise.

Confidential patient information worth big money, a vast network of connected medical devices, outdated technology, among other factors, make the healthcare industry a constant target of cyberattacks.

Predictive analytics tools and machine learning help calculate real-time risk scores for different transactions and requests, making the system respond differently based on how the event is scored.

David McNeely from the Institute for Critical Infrastructure Technology says: “Once the risk score has been determined in real-time, the system can use this during a login event to either grant the access for a low-risk event or to challenge for Multi Factor Authentication [MFA] or possibly block the access for high-risk events.”

Create risk scores for chronic diseases

Early identification of individuals with a higher risk of developing chronic illnesses is essential for two reasons. It gives care providers and patients the best chance of preventing long-term health issues. It also helps mitigate the potential cost and complexities of the treatment.

By creating a risk score—from examining patients with identical characteristics, gathering lifestyle and clinical data and using algorithms to understand how various factors effect patient outcomes—healthcare providers gain insight into the type of therapy and wellness activities which can benefit their patients.  

Summary

As far as health management is concerned, prediction is the foundation for prevention and treatment. Predictive analytics helps healthcare providers in different ways. In addition to those mentioned above, the technology helps identify individuals likely to miss a clinical appointment and send timely reminders, manage supply chain to enhance efficiency and cut down on unnecessary costs, develop effective therapies and new medication, improve patient engagement and more.

Given its manifold benefits, it’s no wonder that, according to a 2017 study by the society of actuaries, 89 percent of healthcare providers were then either already using predictive analytics in their organizations or planned to in the next five years.

The post Top 5 use cases of Predictive Analytics in Healthcare appeared first on Indium.