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: Read on to learn 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:

• 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.

• 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 IT experts today!

Step By Step Guide To Help You Choose The Best Infrastructure For SAP HANA

Businesses today are undertaking the move to SAP HANA following the 2025 deadline by the Enterprise giant SAP who assuredly states that after 2025, all its’ systems will be built to run on only a single database, which is SAP HANA.

Why Choose SAP HANA

Along with providing a single and secure environment for all your mission-critical data assets, SAP HANA can ensure an increased improvement in the total cost of ownership as it is capable of managing large chunks of structured as well as unstructured data.

SAP HANA is tailored to enhance business outcomes as it is one of the prime data management platforms that came into being first and also is competent enough to handle all transactions and also memory analytics on a single data copy. Data integration and quality are other 2 key characteristics.

On a second note, if machine learning and predictive analysis along with advanced analytical processing are paramount for your business, then SAP HANA is definitely the best choice as your data management platform.

In this era of digital reinvention, SAP HANA can reduce administrative efforts considerably now and in the future by rapidly improving application development capabilities in today’s digital landscape.

Related Reading: Read on to learn how SAP HANA adoption is the new game changer trend. 

Ways To Choose SAP HANA As A Digital Growth Strategy

S/4HANA can be an integral part of a digital deployment and management platform for creating innovation and building business value for your company.

Also, obtaining real-time updates is crucial that SAP HANA provides along with big data analysis. When HANA is implemented into your SAP system, it helps in building an in-memory database which helps in providing faster execution. This can provide a wider digital reinvention strategy now and in the future to increase business outcomes. Let us now look at ways to choose the best infrastructure for SAP HANA and what it can offer to your business:

Related Reading: Find why should you choose SAP and how should you plan your budget for it.

1. Real-time problem solving

Real-time update paves the way into an increased business size. With SAP HANA, the data architectures have moved to complex data structures to provide a business process analysis using these real-time updates. The advanced analytical processing power of SAP HANA gets complex calculations performed in real-time and provides answers to the most pressing concerns in your business.

Also, creating visual insight-filled applications have been made easy with SAP HANA implementation.

2. Big data Analysis

Decisions are well-informed than ever before with the implementation of SAP HANA. Multidimensional analysis that leverages spatial and business data to create applications that overcome the performance of traditional databases.

SAP HANA can be thus used as the enterprise database for market-leading solutions.

3. All-Transactions On A Single Platform

SAP HANA is set up to be incorporated as an in-memory database system. High speed is a major advantage. Data integration and effective analytics contribute to the infrastructure.

Analyzing malicious use of the system and predictive analysis is thus made easier through this high-speed data processing technique.

4. Flexibility

Appliances and Tailored Data-Centre Integration (TDI) are the 2 major types of HANA platforms of SAP. This includes a pre-integration of both hardware and software systems. The integration of hardware can provide increased performance and response times. This also ensures key features such as peak performance, memory space, disk space, average load, CPU space, etc. These values are based on the various business processes, a number of users, other factors such as data retention times and much more.

5. Resilience Power

If long term performance is one among your core requisites, the SAP HANA requires the right infrastructure for faster in-memory execution. A proper environment and infrastructure can support application migration as well with ease before failures can happen.

For this, it is important to ensure that the data is not changing rapidly and unpredictably as it can cause the in-memory to slow down considerably.

6. Scalability

Statistics show that SAP HANA, though slow initially can increase the natural growth of structured data by about 20 percent yearly. Unlike other platforms, SAP HANA can take all the data onto the same platform, thus enabling scalability, in the long run.

This feature as it provides scalability feature for the company, it also provides the company with an added advantage of not having to implement fragmented deployments or other complex settings.

7. SAP Suite Of Comprehensive Services

 Big data analytics, a well-optimized and simple data structure, and an incorporated in-memory database together under a single roof ensure a suite of comprehensive business services from SAP HANA that enables users to work with high speed and virtualization capability.

8. S/4 HANA Suite

When a user needs to migrate, the Suite on HANA is readily available. A Suite is a tool that can be used for migrating from databases such as Oracle to SAP HANA. It provides users with optimized objects and code that yields a better and improved business performance and migration.

9. Accelerated Insights

Multi-threading features in SAP HANA ensure the availability of insights for your business. This helps in analyzing what changes are to be made to the existing system or what new features to be incorporated are.

10. Spatial Solutions

The advanced analytical power of SAP HANA can help in real-time calculations. This helps in building a better forecast on how to approach your business for profitable outcomes and also to understand what SAP HANA integration can do to your newly transformed system. Spatial and business data are thus leveraged to gain solutions for a faster business process.

11. Data Protection

Business analytical processes demand protection. SAP HANA is well-structured to store hybrid applications from entry-level to large businesses. This also reduces data center footprints and increases performance.   

SAP Insider has introduced ‘Iterative Sizing’ feature to analyze and measure system requirements. They are a 5-step process and is described as follows:

• Create a sizing project and enter relevant data.
• Obtain an initial sizing result for CPU, disk, and memory.
• Apply according to all SAP HANA guidelines.
• Check to verify all hardware configurations as provided in the SAP HANA directory.
• Provide the required SAP HANA vendor.

These 5 steps provide all the required steps to gather information about the infrastructure.

Related Reading: SAP Vs Oracle- Find who is winning in the race.

The quality operations of SAP HANA are essential as a good level of infrastructure build is required for the transfer of data to the server RAM from the disks. This can successfully implement SAP HANA into your business process. To get personalized assistance on choosing the right infrastructure for SAP HANA, get in touch with us today!