Tag: Machine Learning

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. 

Understanding the Importance of Times Series Forecasting 

To be able to see the future. Wouldn’t that be wonderful! We probably will get there someday, but time series forecasting gets you close. It gives you the ability to “see” ahead of time and succeed in your business. In this blog, we will look at what time series forecasting is, how machine learning helps in investigating time-series data, and explore a few guiding principles and see how it can benefit your business.

What Is Time Series Forecasting?

The collection of data at regular intervals is called a time series. Time series forecasting is a technique in machine learning, which analyzes data and the sequence of time to predict future events. This technique provides near accurate assumptions about future trends based on historical time-series data.

The book Time Series Analysis: With Applications in R describes the twofold purpose of time series analysis, which is “to understand or model the stochastic mechanism that gives rise to an observed series and to predict or forecast the future values of a series based on the history of that series.” 

Time series allows you to analyze major patterns such as trends, seasonality, cyclicity, and irregularity. Time series analysis is used for various applications such as stock market analysis, pattern recognition, earthquake prediction, economic forecasting, census analysis and so on. 

Related Reading: Can Machine Learning Predict And Prevent Fraudsters?

Four Guiding Principles for Success in Time Series Forecasting

1. Understand the Different Time Series Patterns

Time series includes trend cycles and seasonality. Unfortunately, many confuse seasonal behavior with cyclic behavior. To avoid confusion, let’s understand what they are:

• Trend: An increase or decrease in data over a period of time is called a trend. They could be deterministic, which provides an underlying rationale, or stochastic, which is a random feature of time series.
• Seasonal: Oftentimes, seasonality is of a fixed and known frequency. When a time series is affected by seasonal factors like the time of the year or the day of the week, a seasonal pattern occurs.
• Cyclic: When a data exhibit fluctuates, a cycle occurs. But unlike seasonal, it is not of a fixed frequency.

2. Use Features Carefully

It is important to use features carefully, especially when their future real values are unclear. However, if the features are predictable or have patterns you will be able to build a forecast model based on them. Using predicted values as features is risky as it can cause substantial errors and provide a biased result. Properties of a time series and time-related features that can be calculated could be added to time series models. Mistakes in handle features could easily get compounded resulting in extremely skewed results, so extreme caution is in order.

Related Reading: Machine Learning Vs Deep Learning: Statistical Models That Redefine Business

3. Be Prepared to Handle Smaller Time Series

Don’t be quick to dismiss smaller time series as a drawback. All time-related datasets are useful in time series forecasting. A smaller dataset wouldn’t require external memory for your computer, which makes it easier to analyze the entire dataset and make plots that could be analyzed graphically.

4. Choose The Right Resolution

Having a clear idea of the objectives of your analysis will help yield better results. It will reduce the risk of propagating the error to the total. An unbiased model’s residuals would either be zero or close to zero. A white noise series is expected to have all autocorrelations close to zero. In other words, choosing the right resolution will also eliminate noisy data that makes modeling difficult.

Types of Time Series Data and Forecasts

Times series basically deals with three types of data –  time-series data, cross-sectional data, and pooled data, which is a combination of time series data and cross-sectional data. Large amounts of data give you the opportunity for exploratory data analysis, model fidelity and model testing and tuning. The question you could ask yourself is, how much data is available and how much data am I able to collect?

There are different types of forecasting that could be applied depending on the time horizon. They are near-future, medium-future and long-term future predictions. Think carefully about which time horizon prediction you need.

Organizations should be able to decide which forecast works best for their firm. A rolling forecast will re-forecast the next twelve months, whereas the traditional, or a static annual forecast creates new forecasts towards the end of the year. Think about whether you want your forecasts updated regularly or you need a more static approach.

By allowing you to harness down-sampling and up-sampling data, the concept of temporal hierarchies can mitigate modeling uncertainty. It is important to ask yourself, what temporal frequencies require forecasts?

Keep Up With Time

As businesses grow more dynamic, forecasting will get increasingly harder because of the increasing amount of data needed to build the Time Series Forecasting model. Still, implementing the principles outlined in this blog will help your organization be better equipped for success. If you have any questions on how to do this, just drop us a message. 

Data Mining Vs Predictive Analytics: Learn The Difference & Benefits

With big data becoming the lifeblood of organizations and businesses, data mining and predictive analytics have gained wider recognition. Both are different ways of extracting useful information from the massive stores of data collected every day. Often thought to be synonyms, data mining and predictive analytics are two distinct analytics methodologies with their own unique benefits.

This blog examines the differences between data mining and predictive analytics. 

Difference Between Data Mining and Predictive Analytics

Data mining and predictive analytics differ from each other in several aspects, as mentioned below:

Definition

Data mining is a technical process by which consistent patterns are identified, explored, sorted, and organized. It can be compared to organizing or arranging a large store in such a way that a sales executive can easily find a product in no time. Various reports state that by 2020 the world is poised to witness a data explosion. Therefore, data mining is a strategic practice that is necessary for successful businesses. It helps marketers create new opportunities with the potential for rich dividends for their businesses. 

Predictive analytics is the process by which information is extracted from existing data sets for determining patterns and predicting the forthcoming trends or outcomes. It uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. In other words, the aim of predictive analytics is to forecast what will happen based on what has happened. 

Techniques and Tools

Although there are many techniques in vogue, data mining uses four major techniques to mine data. They are regression, association rule discovery, classification, and clustering. These techniques require the use of appropriate tools that have features like data cleansing, clustering, and filtering. Python and R are the two commonly used programming languages in data mining.

Unlike data analytics, which uses statistics, predictive analytics uses business knowledge to predict future business outcomes or market trends. Predictive analytics uses various software technologies such as Artificial Intelligence and Machine Learning to analyze the available data and forecast the outcomes.

Purpose

Data mining is used to provide two primary advantages: to give businesses the predictive power to estimate the unknown or future values and to provide businesses the descriptive power by finding interesting patterns in the data.

Predictive analytics are used to collect and predict future results and trends. Although it will not tell businesses what will happen in the future, it helps them get to know their individual consumers and understand the trends they follow. This, in turn, helps marketers take necessary, action at the right time, which in turn has a bearing on the future.

Related Reading: Predictive Analytics: The Key to Effective Marketing and Personalization 

Functionality

Data mining can be broken down into three steps. Exploration, wherein the data is prepared by collecting and cleaning the data. Model Building or Pattern Identification by which the same dataset is applied to different models, thus enabling the businesses to make the best choice. Finally, Deployment is a step where the selected data model is applied to predict results. 

Predictive analytics focuses on the online behavior of a customer. It uses various models for training. With the use of sample data, the model could be trained to analyze the latest dataset and gauge its behavior. That knowledge could be further used to predict the behavior of the customer. 

Talent

Data mining is generally executed by engineers with a strong mathematical background, statisticians, and machine learning experts. 

Predictive analytics is largely used by business analysts and other domain experts who are capable of analyzing and interpreting patterns that are discovered by the machines. 

Outcome  

Data mining enables marketers to understand the data. As a result, they are able to understand customer segments, purchase patterns, behavior analytics and so on. 

Predictive analytics helps a business to determine and predict their customers’ next move. It also helps in predicting customer churn rate and the stock required of a certain product. Additionally, predictive analytics enable marketers to offer hyper-personalized deals by estimating how many new subscriptions they would gain as a result of a certain discount, or what kind of products do their customers seek as a complement to the main product they bought from the seller. 

Related Reading: Using Predictive Analytics For Individualization in Retail

Effect of Data Mining and Predictive Analytics on the Future 

The global predictive analytics market is estimated to reach 10.95 billion by 2022. We are now in a period of constant growth, where businesses have already started using data mining and predictive analytics sift through the available data for searching patterns, making predictions and implementing decisions that will impact their business.

Both approaches enable marketers to make informed decisions by increasing productivity, reducing costs, saving resources, detecting frauds, and yielding faster results. To make the best use of data mining and predictive analytics, you need the right guidance and the best expertise. Talk to our experts and find out how Fingent, top custom software development company can help your business scale up with the power of data. Get on your way to a digital-first future with us.  

How AI and Machine Learning are Driving Cyber Security in FinTech?

Being a subset of the financial services domain, FinTech is targeted by hostile cyber villains. Industries thus require secure mechanisms to keep their data safe and secure. Preventing data losses are critical for Fintechs. 

The World Economic Forum states that cyber-security is the Number One risk associated with the financial services industry.   

Cyber Security Risks Associated With FinTech

Cybersecurity has remained a pressing concern for FinTech. Ever since the global financial crisis of 2008 that challenged the traditional financial institutions significantly, technology-driven start-ups have started evolving increasingly to cater to finance, risk management, digital investments, data security, and so on. Presently, we are in the FinTech 4.0 era. 

The major cybersecurity risk that enterprises implementing FinTech face are from integration issues such as data privacy, legacy, compatibility, etc. Hackers target FinTech as they handle large volumes of customer data that include personal, financial, and other critical information.

FinTech offers a multitude of easily accessible services via its APIs. For instance, API banking. Here, the APIs are developed for the banks to access the FinTech platforms. It becomes open, API banking when open APIs enable third-party developers to build banking applications and services. 

Let us walk through the major cybersecurity challenges triggered by FinTech.

• Data Integrity Challenge

Mobile applications deployed for FinTech services play a predominant role in cybersecurity assurance. FinTech services require strong encryption algorithms to avoid integrity issues that can arise while transferring financial data. 

• Cloud Environment Security Challenge

Cloud computing services such as Payment Gateways, Digital Wallets including other secure online payment solutions are key enablers of the FinTech ecosystem. Though it is simple to make payments via cloud computing, it is equally crucial to maintain the security of data as far as banks are concerned. Appropriate cloud security measures are thus critical while dealing with sensitive information.

• Third-Party Security Challenge

Third-party security challenges include data leakage, service challenges, litigation damages, and so on. Banks should be aware of FinTech service relationships while associating with third-parties. 

• Digital Identity Challenges

Major FinTech applications are web apps that have mobile devices working at the front-end. Banks and other financial institutions need to learn about the security architecture of the electronic banking services offered by these applications before implementing the FinTech application.

• Money Laundering Challenges

The use of cryptocurrency for financial transactions makes FinTech-drive banks prone to money laundering activities. Thus, the FinTech ecosystem needs to be formally regulated based on global standards.

• Blockchain Challenges

Private keys can be stolen in case of weak security in blockchain architecture. Cryptographic algorithms need to be strong and transactions need to be confidential.  

The increase in the number of FinTech implementation of interfaces will cause a rise in the number of cybersecurity challenges as well. 

Can Machine Learning Predict And Prevent Fraudsters?

How Artificial Intelligence And Machine Learning Enables Cyber Security For FinTech?

Artificial Intelligence is both reactive as well as proactive or preventative. AI reinvents FinTechs by bringing in behavioral biometrics solutions. These solutions are used to monitor customer and device interactions that take place during transactions that enhance security and authentication. BB or behavioral Biometrics with AI provides problem-solving capabilities for FinTechs. FinTechs utilize Artificial Intelligence is an expert system that enhances decision-making abilities using deductive reasoning. Big Data analytics is used here to focus on quality data. 

The underlying technology in using Artificial Intelligence involves reasoning, learning, perception, problem solving, and linguistic intelligence to provide critical insights. It helps in understanding business in real-time operations. 

In this digital era of increasing cybersecurity attacks and malpractices, AI can be used effectively to prevent risks and attacks. The following are major ways of how AI and ML protect FinTechs:

1. Fraud Detection

AI and machine learning algorithms are used to detect frauds in FinTechs by being able to identify transactions in real-time accurately. The traditional strategy of fraud detection involved analyzing large volumes of data against sets of defined rules using computers. This process was time-consuming and complex. Unlike this traditional method, more intelligent data analytics tools for fraud detection such as KDD (Knowledge Discovery In Databases), Pattern Recognition, Neural Networks, Machine Learning, Statistics, and Data Mining have evolved. 

2. Controlling Access

Access control to critical data is crucial when it comes to security. Machine learning is used to derive critical insights from previous behavioral patterns such as geolocation, log-in time, etc to control access to endpoints. The risk scores are fine-tuned by combining supervised and unsupervised machine learning methods to reduce fraud and thwart breach attempts as well. 

3. Smart Contracts

Smart contracts are coded in a programming language and stored on the blockchain. With blockchain, new contracts can be added to existing ones without having to change the individual contracts, in case of agreement expansion. Artificial Intelligence has become an integral part of FinTech as more traditional banks are teaming up with FinTechs to leverage the benefits of both worlds. For instance, when customers face issues with a poor credit history while applying for loans. 

Artificial Intelligence is yet to be transforming the face of FinTechs in a multitude of ways. Drop-in a call right away and our strategists will guide you on how to leverage the benefits of AI and ML to secure operations and prevent breach attacks.

Key Differences Between Machine Learning And Deep Learning Algorithms

Artificial Intelligence is on the rise in this digital era. According to IDC’s latest market report, global investment of businesses in AI and cognitive systems is increasing and will mount to $57.6 billion by the year 2021. 

Artificial Intelligence holds a high-scope in implementing intelligent machines to perform redundant and time-consuming tasks without frequent human intervention. AI’s capability to impart a cognitive ability in machines has 3 different levels, namely, Active AI, General AI, and Narrow AI. Artificially intelligent systems use pattern matching to make critical decisions for businesses.

Related Reading: Know the different types of Artificial Intelligence.

Categories Of Artificial Intelligence

Machine learning and Deep learning are 2 categories of AI used for statistical modeling of data. The paradigms for the 2 models vary from each other. Let us walk through the key differences between the two:

• Machine Learning: Process Involved

Machine learning is a tool or a statistical learning method by which various patterns in data are analyzed and identified. In machine learning, each instance in a data set is characterized by a set of attributes. Here, the computer or the machine is trained to perform automated tasks with minimal human intervention. 

To train a model in a machine learning process, a classifier is used. The classifier makes use of characteristics of an object to identify the class it belongs to. For instance, if an object is a car, the classifier is trained to identify its class by feeding it with input data and by assigning a label to the data. This is called Supervised Learning. 

To train a machine with an algorithm, the following are the standard steps involved:

• Data collection  
• Training the Classifier
• Analyze Predictions 

While gathering data, it is critical to choose the right set of data. This is because it is the data that decides the success or failure of the algorithm. This data that is chosen to train the algorithm is called feature. This training data is then used to classify the object type. The next step involves choosing an algorithm for training the model. Once the model is trained, it is used to predict the class it belongs to. 

For instance, when an image of a car is given to a human, he can identify it belongs to the class vehicle. But a machine requires to be trained via an algorithm to predict that it is a car through its previous knowledge. 

Various machine learning algorithms include Decision trees, Random forest, Gaussian mixture model, Naive Bayes, Linear regression, Logistic regression, and so on. 

Machine Learning- Deciphering the most Disruptive Innovation : INFOGRAPHIC

• Deep Learning: Process Involved

Deep learning can be defined as a subcategory of machine learning. Inspired by ANN (Artificial Neural Networks), deep learning is all about various ways in which machine learning can be executed. Deep learning is performed through a neural network, which is an architecture having its layers, one stacked on top of the other.

A neural network has an input layer that can be pixels of an image or even data of a particular time series. The next layer comprises of a hidden layer that is commonly known as weights and learns while the neural network is trained. The final layer or the third layer is that predicts the result based on the input fed into the network. 

The neural network thus makes use of a mathematical algorithm to predict the weights of the neurons. Additionally, it provides an output close to the most accurate value. 

Automate Feature Extraction is a way in which process performed to find a relevant set of features. It is performed by combining an existing set of features using algorithms such as PCA, T-SNE, etc. For instance, to extract features manually from an image while processing it, the practitioner requires to identify features on the image such as nose, lips, eyes, etc. These extracted features are fed into the classification model. 

The process of feature extraction is performed automatically by the Feature Extraction process in Deep Learning by identifying matches. 

Related Reading: AI and ML are revolutionizing software development. Here’s how!

Key Differences Between Machine Learning And Deep Learning Algorithms

Though both Machine Learning and Deep Learning are statistical modeling techniques under Artificial Intelligence, each has its own set of real-life use cases to depict how one is different from the other. Let us walk through the major differences between the modeling techniques.

1. Data Dependencies

Machine learning algorithms are employed mostly when it comes to small data sets. Even though both machine learning and deep learning can handle massive amounts of data sets, deep learning employs a deep neural network on the data as they are ‘data-hungry’. The more data there is, the more will be the number of layers, that is the network depth. This increases the computation as well and thus employs deep learning for better performance when the data set sizes are huge.

2. Interpretability

Interpretability in Machine Learning refers to the degree to which a human can understand and relate to the reason and rationale behind a specific model’s output. The major objective of Interpretability in machine learning is to provide accountability to model predictions. 

Certain algorithms under machine learning are easily interpretable, such as the Logistic and Decision Tree algorithms. On the other hand, Naive Bayes, SVM, XGBoost algorithms are difficult to interpret. 

Interpretability for deep learning algorithms can be referred to as difficult to nearly impossible. If it is possible to reason about similar instances, such as in the case of Decision Trees, the algorithm is interpretable. For instance, the k-Nearest Neighbors is a machine learning algorithm that has high interpretability.

3. Feature Extraction

When it comes to extracting meaningful features from raw data, deep learning algorithms are the most suitable method. Deep learning does not depend on binary patterns or a histogram of gradients, etc., but it extracts hierarchically in a layer-wise manner. 

Machine learning algorithms, on the other hand, depend on handcrafted features as inputs to extract features. 

4. Training And Inference/ Execution Time

Machine learning algorithms can train very fast as compared to deep learning algorithms. It takes a few minutes to a couple of hours to train. On the other hand, deep learning algorithms deploy neural networks and consumes a lot of inference time as it passes through a multitude of layers. 

5. Industry-Readiness

Machine learning algorithms can be decoded easily. Deep learning algorithms, on the other hand, are a black box. Machine learning algorithms such as linear regression and decision trees are made use of in banks and other financial organizations for predicting stocks etc. 

Deep learning algorithms are not fully reliable when it comes to deploying them in industries. 

Both machine learning and deep learning algorithms are used by businesses to generate more revenue. To know more about how your business can benefit from artificially intelligent systems and which algorithms can be leveraged for a positive business outcome, call our strategists right away!

How Machine Learning Edges Us Closer to Paperless Office?

Paper! Paper! Everywhere! Until recently you couldn’t imagine an office without paper. But today, Machine Learning allows you to print, sign, fill and scan digitally. It eliminates the hassle of handling multiple paper documents and helps organizations in converting to a paperless office.

In this blog, we will discuss how ML is influencing the modern workplace, the importance of paperless office and the industries which are seeing a tremendous impact through paperless technology.

The Role of ML in Achieving a Paperless Workplace

Machine Learning (ML) which is a subset of Artificial Intelligence (AI), is a science of software application where the program can learn to provide accurate outcomes without detailed coding. Through reinforcement signals, the software is able to “learn” the best possible approach to achieve the desired goal. Machine Learning algorithms are being trained to take on collaborative business processes and workflows for automation. This enables employees and the organization to go digital. 

Machine Learning replaces huge filing cabinets and the laborious process of searching for the right information. To find information easily, to collaborate and manage a business more effectively, ML uses powerful search and discovery tools. Since computers have the ability to process calculations, scan large amounts of data, and assess probabilities in a matter of seconds, Machine Language (ML) is proving to be an extraordinary innovation that will greatly impact the workplace. Let us consider some aspects of office organization and how ML is superior to the traditional paper workflow. 

Related Reading: AI and ML are revolutionizing software development. Here’s how!

• Efficient Document Organization

You save time in searching for documents. Information is readily accessible to all employees. Restricting access to confidential documents is made easier. You could access digital documents from anywhere which facilitates remote working. The origin of digital documents can also be traced easily.

• Enhanced Security 

Customers are often concerned about data protection. This requires that companies provide greater security beyond paper shredders and locked filing cabinets. The digital format offers greater document security. Since it is inexpensive to create backups, it is easier to retrieve lost or stolen data. 

• Lower Overhead Costs

Research estimates that an office worker makes more than 60 trips per week to the printer, fax machine, and copier. Digitizing documents eliminates those trips as well as the need to buy expensive equipment and pay for their maintenance. This has a direct impact on reducing operating costs. Digital documents could be sent across by electronic mail, saving postal costs. 

• Lesser Storage Space

A paperless office software frees up space. Companies now can archive everything on private company servers or in the cloud. Jonathan Velline, executive vice president for ATM banking and store strategy at Wells Fargo, talks about the benefits achieved by utilizing paperless document management tools and wireless devices: “It’s a very efficient use of space for us. In a 3,000 square-foot store, we would have an area for full-service banking and a separate area for self-service banking. Here we fit it all in one place.” Having a fully integrated paperless system, employees don’t have to have designated offices. Mini work areas inside the store are more than enough to digitally access customer information and any other details required. This way, Wells Fargo, reduced their office space to three times smaller than the average location.

Related Reading: You may also like to take a look at the top AI trends of the year!

How has Machine Learning Helped Industries to Go Paperless?

Machine Learning has found application in many industries and has helped them in going paperless. Let’s consider three such formerly paper-heavy sectors – legal firms, the automobile industry, and the insurance sector:

Legal firms

ML facilitates greater efficiency and productivity by allowing a lawyer to shift his focus from labor-intensive tasks to core functions like counseling, analysis, and advocacy. Since it is capable of eliminating the laborious process of managing and reviewing boilerplate documents within legal contracts, it allows time for attorneys to appear in court, advice their clients, and negotiate deals. ML can also generate alerts to provide advance notification regarding crucial dates in contracts, such as renewal dates. It can reduce the overall cost of litigation in many ways. It reduces the amount of time a lawyer spends on proofing a document and helps locate relevant information quickly. Use of computer algorithms also helps an attorney identify relevant information that is buried in electronic documents. ML is further equipped to provide a paper-free trial for legal firms. 

Automobile industries

Machine Learning enables machines and devices to replicate the way humans learn. This has enabled great strides in the automobile industry in terms of supporting a paperless office. Machine Learning is also capable of generating highly sensitive autonomous systems that can speed up the process of filing claims if an accident occurs, eliminating the time consuming and paper-heavy process of filling up elaborate forms.

With ML algorithms, the automotive industry is set to have various features like automatic braking, pedestrian, collision avoidance systems, and cyclists’ alerts. It also supports dealers and manufacturers by enabling a paperless update of the vehicle’s firmware. Through the cloud, a diagnostic system can communicate any problems by sending performance data directly to the manufacturer or schedule repairs. 

Insurance

Insurers are using Machine Learning to boost customer service, increase their operational efficiency, and even detect fraud.  ML can improve the process of insurance and automatically move claims through the system. With sophisticated rating algorithms, companies are able to fit in most risks as long as they find good pricing. ML can support agents in classifying risks and calculating accurate predictive pricing models. Tools powered by ML, help consolidate volumes of highly varied data such as membership and provider data, insurance claims data, benefits, and medical records without the use of paper. These solutions can process and structure data with insights leading to a higher quality of care, costs reduction, and fraud detection.

Insurers can draw insights from data about behaviors, individual preferences, lifestyle details, attitudes, and hobbies to create personalized products such as loyalty programs, policies, and recommendations. 

Machine Learning- Deciphering the most Disruptive Innovation : INFOGRAPHIC

Go Paperless Now!

The call to move to a paperless office is getting more urgent every day. To make this transition easy, we can help your organization reap the best benefits of Machine Learning.  Give us a call and let’s talk!

How Machine Learning Systems Detect And Prevent Frauds Without Affecting Your Customers

There is nothing more fearful than imbalanced data, especially when dealing with various payment channels like credit and debit cards in banks and other financial organizations. With the wide increase of different payment mediums, businesses are finding it difficult to authenticate transactions. But Machine Learning has been a viable solution to detect fraudsters.

Machine Learning can be referred to as the ability of machines to learn data with the help of human intelligence as well. According to the latest report by Gartner, by 2022, more than nearly half the data and analytics services/ tasks will be done by machines.

Related Reading: How machine learning can help boost customer experience. 

Machine Learning In Making Real-Time Decisions To Prevent Fraud Activities

If a business is able to predict which transactions can lead to fraudster attacks, then the business can considerably lower costs and make critical decisions. While sending sensitive data to a third-party, it is important that the data is not misused for fraudulent activities. This can be done as follows:

1. Using Machine Learning Models

Consider a score produced from a number of algorithms that is a combination of all possible features. This set of algorithms can be termed as a machine learning model. This machine learning model constantly queries these algorithms in order to produce an accurate score that can be used to predict frauds.

Machine learning models can be compared to data analysts who run numerous queries on large volumes of data and try finding out the best from the derived outcomes. Machine Learning makes the whole process fast and accurate.

2. Fraud Scores For Fraud Detection

There always exists large amounts of data. Machines are trained using these data sets that are pre-labeled as frauds. These labels are based on earlier records of confirmed fraudulent activities.

The machines are then trained using this labeled set of data. These data sets are now called as training sets. By a named label, the machine is taught to determine if a new transaction or a particular customer is likely to be a fraudster based on a score of 0 to 100, being the probability.

This score enhances the ability of a business to ensure a considerable reduction in frauds by providing accurate predictions.

Related Reading: Check on to this Infographic to learn more about Machine Learning.

Can Machine Learning Actually Predict And Prevent Fraudsters?

Designing as well as being able to apply algorithms that are on the basis of data sets from the past, enables to analyze frequent patterns in these data sets. These patterns in data via the algorithm are taught to machines and these machines considerably reduce human effort.

These algorithms help businesses boost predictive analysis. Predictive analysis is important for data reduction by using statistical modeling techniques that help in predicting future business outcomes on the basis of past data patterns. In fact, among many businesses, 75 percent of them find growth to be their main source of value, whereas 60 percent of some others believe that it is nothing else but predictive analytics that is the key to deriving value!

Machine learning algorithms are not only used in predictive analytics, but also in image recognition, detecting spam, and so on. Machine Learning can be trained by a 3 phase system.

1. Train
2. Test
3. Predict

So to be able to predict an occurrence of fraud in large volumes of data sets and transactions, cognitive technologies of computing are applied to raw and unprocessed data.

Machine Learning thus facilitates, prediction and prevention of fraudsters for the following key factors:

• Scalability: Larger the data sets, increased is the effectiveness of machine learning algorithms. Initially, the machine learns which transaction/data sets are fraudulent and which ones are safe, the machines are well able to predict such cases in future transactions.
• Readiness: Manual tasks are time-consuming. These are not preferred by clients. Hence, machine learning strategies are used to acquire faster results. Machine learning algorithms process a large number of data sets in real-time to customers. Machine Learning frequently and periodically analyzes and processes new data sets. Advanced models like neural networks have provisions for autonomous updations in real-time.
• Productivity: The need to perform redundant tasks reduces productivity. The continuous repetitive task of data analysis is performed by Machine Learning algorithms and prompts for human intervention only when required.

Related Reading: Check out how machine learning is revolutionizing software development.

Machine Learning Methods – Using White Boxes And Ongoing Monitoring To Detect Fraudsters

What does a machine learning system do? The methods adopted and the various approaches used for this are termed Whiteboxes, as there is no definite method or model to analyze the score obtained. Similarly, regular and ongoing monitoring is critical for a machine learning system to identify the trends and data statistics on a regular basis.

How Fraudsters Are Detected And Prevented By Using Machine Learning

Data sets are initially collected and partitioned. The machine learning model is taught the sets in order to predict data fraud. The following are the steps in which Machine Learning implements and performs fraud detection:

• Data Partitioning: The data is segmented into working in three different phases such as training the machine, testing for data sets and finally, cross-checking of the prediction results.
• Obtaining Results of Historical Data: To obtain such data sets, training sets have to be first provided to the machine that includes input values associated with its corresponding output values. This helps in predicting and detecting frauds.
• Predicting Anomalies, If Any: Based on the input and output data, predictions are determined by analyzing the anomalies or fraud cases in the data sets. For this, building models are used. This can be done by many techniques such as using Decision Trees, Logistic Regression, Neural Networks, and Random Forests, etc.
• Out of the techniques, Neural Networks are quick in processing results by analyzing data sets and helps in making decisions in real-time. It does so by observing regular patterns of frauds in earlier cases of data sets given to it for learning.

In a nutshell, Machine Learning is proving to be the right technology in detecting and preventing fraudsters from malicious activities. If banks start using machine learning systems, it could analyze unstructured data and prevent customer’s accounts from fraudulent activities. To know more about how you can empower machine learning and other technology trends to secure data, get in touch with our custom software development experts today!

Artificial Intelligence (AI) is considered to be one of the most significant disruptive technologies today. More and more businesses are already realizing its benefits. Gartner’s 2019 CIO survey revealed that the percentage of companies implementing AI increased by about 270 percent over the last four years, and 37 percent in 2018 alone.

Leveraging the power of AI to enhance your existing business applications isn’t nearly as complicated as you might think. You don’t need a billion-dollar budget to implement AI-powered applications. In fact, small and midsize businesses (SMBs) today are cutting costs and delivering great customer experiences with AI-powered applications—and they are competing with giant companies at scale.

Here’s a look at how you can enhance your existing business applications with AI:

Enhance CRM Apps with AI

Incorporating AI into your current Customer Relationship Management (CRM) system, for instance by using chatbot or automated live chat support, will allow your company’s helpdesk to provide better, faster and more dynamic responses. It will also help you reduce the man-hours needed to resolve queries and help you build better engagement and customer trust. And because the AI-powered CRM system provides predictive insights, you can automatically recommend similar products or services a customer may be interested in.

Related Reading: Unconventional Ways Artificial Intelligence Drives Business Value

Streamline Supply Chain with Machine Learning

Machine learning (ML) allows your system to discover patterns in the supply chain data using algorithms that automatically identify the factors that contribute to the success of your supply networks, while constantly learning in the process. ML algorithms and the applications running them can analyze large, varied data sets in no time, improving accuracy in forecasting supply and demand. If applied correctly within your SCM work tools, ML could revolutionize the agility and optimization of your supply chain planning.

AI-Powered Recruitment Apps

Artificial Intelligence is expected to replace 16 percent of Human Resource (HR) jobs within the next 10 years, according to Undercover Recruiter. Integrating AI into your existing recruitment processes or tools could help your company’s HR department find the right candidate or the best fit faster and easier, thereby saving you time and money. AI-powered video interview tools, for instance, can utilize biometric and psychometric analysis to evaluate your applicants’ tone of voice, micro-expressions, and body language.

Related Reading: AI To Solve Today’s Retail Profit Problems

Improving Cybersecurity System with AI

Given the data breaches and cyber-attacks that have hit headlines in recent years, integrating AI into your current security system is vital to protect consumer data, improve trust and deliver true business value. About 71 percent of companies in the US plan to spend more budget on AI and machine learning in their cybersecurity software this year.

AI not only improves your company’s existing detection and response capabilities but also allows new abilities in preventive defense. It enhances and streamlines your security operating model by reducing complex, laborious and time-consuming manual inspection and intervention processes. Because the AI-powered cybersecurity system can self-adjust and learn data over time, you can automatically detect and block cyber-attacks and fraud.

Enhancing Space Exploration with AI

Another area where the application of AI has great potential is exploring outer space. NASA has plans to look for life on other planets, such as Mars, in the very near future. In their Mars 2020 initiative, they will use AI to explore Mars in greater depth, which includes looking for alien lifeforms. Most of us are at least slightly familiar with or aware of NASA’s Opportunity rover, which wrapped up a 14-year Mars mission when it quietly went dark in February 2019. Opportunity, also known as “Oppy,” found evidence that Mars at some point was home to water — a huge discovery.

Going forward with Mars 2020, NASA’s Mars Exploration Program will continue its use of AI for space exploration. In ongoing efforts to evaluate whether Mars is (or was at some point) habitable for humans and other animals, the Mars 2020 rover is equipped with a drill it will use to collect samples of rock and soil. It will store these samples in special tubes that will be collected by a later NASA mission. Read more about the artificially-intelligent robotic arm that will make it all happen.

Related Reading: Industry experts weigh in on the adoption of AI and ML in software development

Taking Your Existing Business Applications to the Next Level with AI

New AI frameworks and tools make provisioning AI capabilities more feasible than ever before. Working with a development partner who has the data science and AI technology experience, creating or updating a business application with AI can be started rapidly, take less time to code, and the resulting application placed into service sooner. Nor would it be necessary to staff for these hard-to-find resources for the long term.

Related Video: Artificial Intelligence – How to navigate AI

Disclaimer: This is an opinion piece. The views expressed in this article are mine and does not represent my employer.

Smart, sentient machines! The latest (well, not really) hype! Look back a week or two, and think about the number of days you went without hearing about how AI is going to change your career, health, medicine, food, travel or whatever. Television, newspapers, and blogs remain constantly flooded with announcements about the imminent disruption <insert field here> that is going to witness due to using AI.

Let me show you some, ahem, examples.

We have here (in the order of increasing horror):

• AI-powered Air Conditioners

• AI-powered Washing Machines

Source – Gizmodo

• AI-powered Suitcases

Source – Indiegogo

• AI-powered Phones

• AI-powered Toilet

Source – The Verge

• AI-powered Underwear!

Okay, I made that last one up. But for a second there, you guys did believe me, right? RIGHT?

That is the sad state of affairs. We are all techies here, and might think “wait, WHAT?”. But the vast majority of the not so technical audience out there sees AI as magic. They see it as something beyond their cognitive ability to process and accept any BS branded as “AI-powered” without questions. Thus, we have this article!

Source – Mashable

So what is the truth with AI? If you dig deep enough, or if you peel off enough layers(pun intended), what is happening?

Before we move on to taking the buzz off of buzzwords, let’s look at some core concepts.

Related Read: Top Artificial Intelligence Trends to Watch Out for In 2019

What is AI?

From wiki, Artificial intelligence is intelligence demonstrated by machines. It is the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

But Really, What Is Artificial Intelligence?

IM[not so H]O, AI is just a buzzword. Really, it is just meaningless jargon. Okay, maybe not meaningless, but it’s still jargon. Don’t believe me? Let me give you some examples:

• Computers playing checkers and beating the best human players was considered AI. Until it was not when it was accomplished around 1994 by Chinook, the checkers-playing computer program.
• Computers playing chess and beating the best human players was considered AI. Until it was not when it was accomplished around 1997 when IBM’s Deep Blue defeated the then world champion, Garry Kasparov.
• Cruise control was considered AI. Until it was not when it started being available in production cars in 1990+(partial) and 2010+(full speed range).
• Automatic parking was considered AI. Until it was not when it started being available in production cars somewhere around 2006.
• Human speech recognition was considered AI. Until it was not when it started being available as Google Assistant, Cortana, Siri, etc. Now we have a real-time speech translation!

Obligatory XKCD.

I could go on, there are quite a few examples of this phenomenon, formally known as(yes, it is so well known that it has a name) the AI effect [wiki].

So a much better definition of AI was put forth by Douglas Hofstadter.

“AI is whatever hasn’t been done yet.”   

 – Douglas Hofstadter

Just Computation

“Every time we figure out a piece of it, it stops being magical; we say, ‘Oh, that’s just a computation’.”

– Rodney Brooks

So, if it’s all just computation, why was it not, well, “computed” earlier?

Yes, computation, or rather, the capacity for computation is the key. A lot of problems were characterized as AI because, at the time, algorithms for solving that were not known yet, or because the resources to compute those were not available yet.

• Availability of Computation Power

Eg. Chess/other games, etc.

Moore’s law and the explosion in storage availability have played a major role in turning the tables. [It is important to note that the tables have not turned completely. Yet. There is so much more ground to cover.]

• Availability of Unbiased Data

Eg. Natural language processing (NLP).

Okay, now you may be thinking “Enough data was not available for speech recognition? This guy is full of BS”, but hear me out. With the explosion of social networks, so much content is created and made freely available that finding huge swaths of unbiased(this is the key here) voice/video of natural speech is available, which in turn has helped the advances in NLP.

• Availability of Infrastructure

I guess I don’t have to mention the improvement in internet speed that happened over the decade. This has accelerated content creation, real-time processing, etc.

So, What is All the Current Hype About?

The hype is not current. There has been huge interest around AI from the time it was first proposed around the 1950s. The sheer number of films about it tells us about how much.

But the current wave of hype and buzz surrounding AI comes from the recent advances made in, drumroll please, Machine Learning.

What is Machine Learning?

Machine learning is

• giving computers the ability to learn
• to find patterns in data
• from experience
• without explicit programming.

ML is essentially about classifying and predicting stuff.

The typical operation is something like:

1. Take some data
2. Learn patterns in the data
3. When presented with new data, classify it for the best guess of what it probably is, based on the “learning” that happened in [2].

Related Read: Machine Learning- Deciphering the most Disruptive Innovation

Meh! So what is the big deal?

Once trained for one purpose, the same ML system can be reused(with additional training) to learn new concepts. This can be done without rewriting the code. Now that is a big deal.

Let’s look at a simple example: Classifying emails.

Traditional programming:

if the email contains "it's never a job, its always a career"

then send to trash;

if the email contains ...

then ...

if the email contains ...

then ...

ML programs:

try to classify some emails;

change self to reduce errors;

repeat;

That was a two-minute primer on Machine Learning. So next time someone starts talking about Artificial I, I hope you feel the pang and say “Excuse me, I think you mean Machine Learning, not AI”.

Source – HubSpot

This month we are covering how creating and fostering mobile-driven digital ecosystem help grow your customer base and provide services effectively. We will also be discussing how zero code platforms ease the pain of mobile app development. Lastly, we cover what happens when Machine Learning meets the business world.

Zero-Code Platforms Ease the Pain of Mobile App Development | Business.com

Enterprises find themselves plagued with issues like apps not performing as expected, apps scoring low on user experience and more. But with the benefits of zero-code being obvious to more and more businesses, more platforms are on its way to becoming mainstream.

Where Do You Fit in the Mobile-Core Digital Ecosystem?| Clutch

With the global proliferation of mobile users, enterprises must now focus on mobile-centric solutions. Such a rapid increase in global mobile usage points to a paradigm shift in the future of digital communications.

Mobile devices now act as the medium for facilitating all kinds of communications and services within an ecosystem. In addition, their improved connectivity options and versatility makes them ideal for all kinds of digital ecosystems.

When Machine Learning Meets the Business World| DZone

Let’s discover what happens when Machine Learning meets the business world. Take a look at how it will transform businesses as well as how it will minimize risks.