Tag: business challenges

Can Robotic Process Automation Rescue Businesses From An Economic Recession

COVID-19 is panning out to be a historic tragedy both for the human race as well as our economy. While most businesses are stalled due to the economic recession induced by COVID-19, there is a ray of hope. Robotic Process Automation (RPA) is rising up as the savior for many businesses by offering recession-proof operations. RPA might be that one alleviating factor that can keep your business and the economy afloat. 

When implemented successfully, RPA can help many sectors experience an undeniable upsurge. How RPA can help businesses get through these unprecedented times? This post takes a look. We will also examine a few automation cases.

Related Reading: Jaw-dropping Facts about Robotic Process Automation

Businesses Can Stay Afloat in These Unprecedented Times

RPA goes beyond allowing businesses to stay afloat. It also helps them respond instantly to drastic changes in demand during such critical situations as COVID-19. It has proven to be an invaluable technology for businesses by ensuring that employees remain productive even if they have to work remotely and from home. 

Here are some benefits of RPA.

Increased flexibility

RPA allows your business to run smoothly despite ever-changing demands while automating manual processes and increasing efficiency. Furthermore, it is helping organizations combat the spread of COVID-19 and is providing customers with timely support as businesses move on to new operating models. For instance, deploying chatbots or automated answering machines in contact centers or help desks can help handle bulk volumes of customer service calls and emails, especially when organizations are forced to function remotely or operate with a limited number of employees. 

Less dependency on the individual employee

Though we can’t deny the need for human intervention in most processes, certain tasks need to be accomplished with precision and speed. Here is where RPA becomes useful as a fast and flexible way to replicate employee-driven processes. RPA enables businesses to automate certain critical processes with greater precision and efficiency. And soon, RPA will free businesses from being dependent on the limitations caused by employees’ absence.

Related Reading: How to accelerate your business growth with Robotic Process Automation

Keep up with production

RPA enables organizations to keep up productivity by providing assistance to an overwhelmed customer using attended automation or front office bots. This frees up employees to attend to other critical aspects of the business. RPA can cater to an increase or decrease in the supply chain demand while allowing you to rely on automated back-office activities.

Industries leveraging RPA

Here are a few examples of industries who have successfully leveraged automation: (examples cited by UiPath)

Healthcare

Health of the workforce:  Updating all relevant data of sick employees in real-time while keeping track of healthy employees could be painstaking. With RPA, bots can be set up to keep track of hospital employees. This minimizes manual errors and ensures employee safety. 

Increasing demand in virus testing: With the increasing demand for virus testing, the wait time for registration also increases. Cleveland Clinic in the United States reduced their patient verification and registration time considerably by deploying an attended robot that collects and prints patient data, thereby reducing hospital backlogs.

Accelerating clinical testing: Filling test reports itself would take about 3 hours per day in the life of a staff nurse. But since Mater Hospital in Dublin automated its process through robots, medical personnel are able to use their precious time taking care of their patients rather than chasing admin tasks. 

Banking and financial services

Surge in trading volume: Global markets have seen a trading surge by about 300% daily. This is tremendously increasing the operational burden. Leveraging automation has ensured business continuity while maintaining high levels of customer satisfaction despite the huge spike of activity in areas such as trade allocations and reconciliations.  

Acknowledging customer complaints:  Acknowledging customer complaints in line with UK regulations has been especially challenging for financial services because of their reduced staff. Automation has allowed the banking and financial services to acknowledge complaints in time in compliance with UK regulatory government requirements.

Retail

Helps HR specialists to remain focused on analysis: COVID-19 has overwhelmed the HR department with a large volume of sick leave requests. Automation of two phases of this process has reduced backlogs in processing leave requests while allowing HR specialists to stay focused on their people.

Supply chains

Optimization of the supply chain: COVID-19 has forced factories to shut down leaving their production line idle. RPA allows companies to optimize their supply chains. This has resulted in accurate delivery estimates, optimized vehicle routes, efficient shipment consolidation as well as accountable sourcing. 

Manufacturing

Reduced manufacturing expenses: Prolonged closures of manufacturing plants have forced manufacturers to cut down their operational costs. RPA automates repetitive and manual tasks thus reducing operational costs.

Related Reading: How Robotic Process Automation Is Revolutionizing Industries?

Weather These Turbulent Times with Robotic Process Automation

Robotic Process Automation will enable businesses to efficiently allocate funds to areas that need greater focus. It can automate qualification ad validation processes, update public health data in real-time, and much more. Educational institutes can also leverage automation to schedule and activate their classes using LMS and eLearning platforms.  

Obviously, COVID-19 has brought about changes in the working style of both the consumers and clients. RPA will cater to their needs and help ensure that businesses can stay afloat during these difficult economic times. 

Download our White Paper: Learn how RPA can bring a difference to your business and how to embrace the disruptive technology to maintain a competitive edge.

How Can You Get Started? 

Start small! Start by automating small tasks that will give you the needed confidence and experience in automation solutions. Research and evaluate how far automation is applicable to your IT environment. Talk to custom software development experts at Fingent and find out how you can build on the benefits of RPA. 

Top 10 Algorithms to Create Functional Machine Learning Projects

From simple day-to-day functions to making computers smarter, Machine Learning algorithms help automate manual tasks for making our lives simpler. The significance of Machine Learning has grown even further, which is why enthusiastic data scientists and engineers look forward to learning different techniques to hone their skills.

Below are the top 10 Machine Learning algorithms that you should know. These will help you to create practical projects, no matter whether you choose Supervised, Unsupervised, or Reinforcement Machine Learning model.

Read our Infographic: What Machine Learning is and why it is important in business

1. Apriori Algorithm

Apriori algorithm is a type of machine learning algorithm, which creates association rules based on a pre-defined dataset. The rules are in the IF_THEN format, which means that if action A happens, then action B will likely occur as well. The algorithm derives such conclusions by analyzing the ratio of action B to action A.

One of the most common examples of the Apriori algorithm can be seen in Google auto-complete. When you type a word, the algorithm automatically suggests associated words that are mostly typed with that.

2. Naive Bayes Classifier Algorithm 

Naive Bayes Classifier algorithm works by presuming that any specific property in a category is not related to the other properties of the group. This helps the algorithm to consider all the features independently as it calculates the outcome. It is very easy to create a Naive Bayes model for huge datasets, and can even do better than many of the complex classification methods.

The best example of the Naive Bayes Classifier algorithm will be email spam filtering. The function automatically classifies different emails as spam or not spam.

3. Linear Regression Algorithm

Linear Regression algorithm determines the correlation between a dependent variable and an independent variable. It helps understand the effect that the independent variable will cause on the dependent variable if the former’s value is changed. The independent variable is also referred to as the explanatory variable, while the dependent variable is termed as the factor of interest.

Generally, the Linear Regression algorithm is used in risk assessment processes, especially in the insurance industry. The model can help to figure out the number of claims as per different age groups and then calculate the risk as per the age of the customer.

Related Reading: Can Machine Learning Predict And Prevent Fraudsters?

4. K-Means Algorithm

K-Means algorithm is commonly used for solving clustering problems. It takes datasets into a specific number of clusters, which is referred to as “K”. The data is categorized in such a way that all the data points in the cluster remain homogenous. At the same time, the data points in one cluster will be different from the data grouped in other clusters.

For instance, when you look for, say, “date”, on the search engine, it could mean a fruit, a particular day, or a romantic night out. The K-Means algorithm groups all the web pages that mention each of the different meanings to give you the best results.

5. Decision Tree Algorithm

Decision Tree algorithm is the most popular Machine Learning algorithms out there today. The model works by classifying problems for both categorical as well as continuous dependent variables. Here, all the possible outcomes are divided into different standardized sets with the most significant independent variables using a tree-branching methodology.

The most common example of the Decision Tree algorithm can be seen in the banking industry. The system helps financial institutions to categorize loan applicants as well as determine the probability of a customer defaulting on his/her loan payments.

Related Reading: How Predictive Algorithms and AI Will Rule Financial Services

6. Support Vector Machine Algorithm

Support Vector Machine algorithm is used to classify data as points in a vast n-dimensional plane. Here, “n” refers to the number of properties in hand, each of which is linked to a specific subset to categorize the data. A common use of the Support Vector Machine algorithm can be seen in the regression of problems. It works by categorizing data into different levels using a particular line or hyper-plane.

For instance, stockbrokers use the Support Vector Machine algorithm to compare the performance of different stocks and listings. This helps them to device the best decisions for investing in the most lucrative stocks and options.

7. Logistic Regression Algorithm

Logistic Regression algorithm helps calculate separate binary values from a cluster of independent variables. It then helps to forecast the likelihood of an outcome by analyzing the data against a logit function. Including interaction terms, eliminating properties, standardizing techniques, and using a non-linear model can also be used to create better logistic regression models.

The probability of the outcome of a specific event in the Logistic Regression algorithm is calculated as per the included variables. It is commonly seen in politics to predict if a candidate will win or lose in the election.

8. K- Nearest Neighbors Algorithm

K Nearest Neighbors or KNN algorithm is used for both the classification as well as regression of different problems. The model saves the data available from several cases, which is referred to as K, and classifies new cases as per the data from the K neighbors based on distance function. The new case is then included in the identified dataset.

K Nearest Neighbors needs a lot of storage space to save all the data from different variables. However, it only functions when needed and can be very reliable in predicting the outcome of an event.

9. Random Forest Algorithm

Random Forest algorithm works by grouping different decision trees based on their attributes. This model can deal with some of the common limitations of the Decision Tree algorithm. It can also be more accurate to predict the outcome when the number of decisions goes higher. The decision trees are mapped here based on the CART or Classification and Regression Trees model.

A common example of the Random Forest algorithm can be seen in the automobile industry. It is seen to be very productive in forecasting the breakdown of a specific automobile part.

10. Gradient Boosting and Adaptive Boosting

Gradient Boosting and Adaptive Boosting (AdaBoost) algorithms can be used when you need to handle a huge amount of data and predict the outcome with the highest accuracy possible. Boosting algorithms combine the power of different basic learning algorithms to improve the results. It can also merge weak or average predictors to get a strong estimator model.

Gradient boosting is generally used with decision trees, while AdaBoost is typically used to improve binary classification problems. Boosting can also correct the misclassifications found in different base algorithms.

The above-listed Machine Learning algorithms will help you get started with your desired projects right away. These will equip you for understanding the scope of Machine Learning as well as work out complex problems more easily.

Related Reading: How Machine Learning Boosts Customer Experience 

Want to develop machine learning applications that deliver better experiences for your users? Connect with us.