Supervised Learning Techniques for Fraud Detection

Supervised learning is a common method of machine learning for fraud detection. In supervised learning, a dataset that has been labeled with the correct output for each example is utilized to train the model. This enables the model to understand the connections between the attributes and the label and to predict outcomes using brand-new, untainted data.

The decision tree is a common supervised learning algorithm type for fraud detection. The predictions made by decision trees are based on a succession of binary splits, with the leaf nodes serving as the ultimate prediction and each internal node representing a decision based on the value of a characteristic. Both numerical and categorical data can be handled by decision trees, and they are simple to grasp and analyze. However, they are sometimes prone to overfitting, particularly if the tree grows to be excessively deep.

Logistic regression is a different class of supervised learning technique that is frequently employed in fraud detection. A linear model called logistic regression is used to forecast a binary outcome, such as whether or not a claim is false. It operates by assessing the likelihood of the event and categorizing it as either “0” or “1” depending on whether the probability is below or over a predetermined threshold. Decision trees are more prone to overfitting than logistic regression, which is easier to execute and interpret. If the relationships between the features and the label are non-linear, it might not function properly.

Unsupervised Learning Techniques for Fraud Detection

Unsupervised learning is another machine learning technique that is useful for fraud detection. In unsupervised learning, the model is not provided with labeled examples, and must instead discover patterns and relationships in the data on its own. One popular unsupervised learning algorithm for fraud detection is the k-means clustering algorithm. This algorithm works by dividing the data into a specified number of clusters, based on their similarity. The assumption is that fraudulent cases will form their own distinct cluster, which can then be identified and flagged. K-means clustering is easy to implement and can handle large datasets, but it is sensitive to the initial conditions and may not always find the optimal solution.

Another unsupervised learning algorithm that is useful for fraud detection is the anomaly detection algorithm. This algorithm works by identifying cases that are significantly different from the majority of the data, and flagging them as potential fraud. Anomaly detection can be useful for detecting rare cases of fraud that may not be identified by other methods. However, it can also produce a high number of false positives, and may not be as effective at detecting more common types of fraud.

Semi-Supervised Learning for Fraud Detection

Another machine learning technique that combines aspects of supervised and unsupervised learning is semi-supervised learning. The model is trained on a partially labeled dataset in semi-supervised learning, and it is required to make predictions on both labeled and unlabeled cases. The support vector machine is a well-liked technique for semi-supervised learning (SVM). SVMs function by locating the hyperplane in a high-dimensional space that best segregates the various classes. They work effectively on a range of activities and are efficient at managing high-dimensional data. However, they might not scale well to very big datasets and their training can be computationally expensive.

Conclusion

In conclusion, there are several different machine learning techniques that can be used for detecting insurance claims fraud. Each technique has its own strengths and weaknesses, and the best approach will depend on the specific characteristics of the dataset and the needs of the insurance company. It is important to carefully evaluate the performance of different machine learning techniques and choose the one that offers the best balance of accuracy, efficiency, and interpretability.

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What is inventory management?

Inventory management is a difficult task, especially for businesses with multiple store locations and retailers who sell thousands of products each month. Order mix-ups, dead stock issues, deficient stock situations, and storehouse complaints are all common problems for retail and e-commerce business. For owners of small and medium-sized businesses, inventory management is a top priority. Reducing overstock and out-of-stock situations will be made easier with the help of a system that monitors inventory levels, orders, and transactions to perform predictive analysis and gather forecasted demand.

5 benefits of inventory management using machine learning

• Stock tracking using machine learning

The stock market is largely responsible for being volatile, dynamic, and nonlinear. Because of numerous (macro and micro) factors, including politics, international economic conditions, unforeseen events, a company’s financial performance, and others, accurately predicting stock prices is very difficult.

With machine learning, current data input is used to modify software-generated calculations and estimates. Using it to improve the precision of stock tracking, optimize inventory storage, and provide open communications throughout the supply chain is a way to improve the performance of tracking technology in inventory management by providing more accurate data to facilitate future planning.

• Inventory management optimization

Inventory optimization is the process of keeping the right amount of inventory on hand to meet demand while keeping logistics costs low and avoiding common inventory issues such as stockouts, overstocking, and backorders. Algorithms can be built to fit specialized limitations that work for your business with the help of artificial intelligence

and machine learning. Particularly in companies with numerous distribution centers, this can be used to improve inventory optimization. It proves to be a more efficient way of managing stock.

• Minimize forecasting errors

Machine learning can be used to cut transportation and warehousing costs by keeping inventory at a lean but comfortable level, and it can forecast demand in the near future, allowing stock to be purchased in time for transactions. This improves client delivery times and, as a result, client satisfaction.

• Limiting idle stock

The concern about stock degrees is one of the major factors influencing inventory management. Forecasts for how much stock to carry are frequently inconsistent when based solely on outdated tracking models. Extra and idle stock essentially represents tied-up money that could be put to better use.

If you stock too much, you risk increasing your costs, but if you stock too little, you risk running out of a product entirely. Finding the perfect balance is a difficult task. Reduce stock levels and avoid stockouts by mastering your lead times, automating tasks with inventory management software, calculating reorder points, and using accurate demand forecasting.

• Enhancing customer experience

The success of small and medium-sized businesses depends on maintaining customer satisfaction. A customer who finds the ordering process difficult, cannot get the stock they require, or consistently receives product late is likely to be dissatisfied and look for a new supplier. Let’s have a look at how to improve customer satisfaction:

· Avoid understocking.

· Have an estimate of seasonal demand

· Boost order fulfillment

· Reduce lead times

· Set sustainable pricing

Wrapping up

Technology updates will saturate the world of inventory in the future. To boost sales and draw customers, this industry is constantly evolving, using technologies like virtual reality, artificial intelligence, digital signage, and even stores with no inventory. And it will only get bigger from here. With the advancement of real-time inventory management systems, retailers now have access to more information about consumer demographics, spending patterns, and other factors. Retailers should try to improve their accuracy with their inventory and continue to appeal to their customers with this constant increase in inventory visibility.

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All great cultures are empowered and metamorphosed by the wisdom of generational planning. Native Americans, for instance, believed that one must act keeping in mind the welfare of the next seven generations following their time: a tenet of homeopathic practice and avowed even today by modern science. These cultures understood the pitfalls of analytical myopia and the need to think ahead of time to realize subsistence and more.

To dilettantes with only a passable knowledge of this subject, the term ‘predictive genomics’ may seem obsolete since all endeavors in the field of genetics are predictive in their intent. However, it is a discipline at the crossroads of various fields like personal genomics, phenology, and bioinformatics alongside many more. Its relevance lies in the understanding of the human phenotype (expressed traits in an individual) as a function of the person’s genotype and his/her environment and being able to integrate the two variables of the function in a population for generations using advanced computational modalities like AI and Machine Learning.

Much of today’s predictive genomics is a result of Genome wide Association studies (these studies were possible due to the rise of biobanks, safekeeps of data sets demonstrating great genomic and trait variation) done over the course of the last two decades, occluding further doubts on the inheritance of complex traits, narrowing them down to the thousands of genetic mutations that are known as SNPs (commonly pronounced ‘snips’). These SNPs are the fundamental unit of biological evolution of all organisms, according to neo-Darwinian theory; mutations in their myriad iterations can make or break a species in the long run as they can be at the core of the inheritance of new survival traits and speciation or the accumulation of traits that lead to extinction. They are random and continually occur around us.

Predating these discoveries, the human genome project was completed, about 20 years ago, bequeathing scientists with a huge amount of data; experts estimate that research in genomics could yield up to 40 exabytes of data. With such a staggering volume of complex data to navigate, AI and ML techniques become obvious candidates for enhancing the efficiency and accuracy of predictive genomics.

Today, pharmacogenetics powered by deep learning algorithms make for an excellent use-case of digitally powered predictive genomics. A study that seeks the correlation between drug response and the individual’s genotype, its research labs sequence and genotype millions of people to understand drug delivery and inhibition across different populations. Such data is groundbreaking in demonstrating the relevance of individual gene expression in the efficacy of various medicines.

Consider the gene CYP2D6, expressed primarily in the liver and is crucial for the metabolization of codeine, one of America’s most popular pain relievers. The gene codes for the synthesis of proteins that converts xenobiotics like codeine to water-soluble morphine through demethylation; it is worthy of note that codeine has no analgesic action by itself. Now, we know through statistical analysis that 1-5% of Americans is poor metabolizers of codeine to the point it elicits no response, while another 1-21% are ultra-metabolizers, leading to huge spikes in blood morphine levels but effective for a very short interval of time. Similar trends can be noted for hundreds of drugs as they are all metabolized by CYP2D6, with its 161+ recognized haplotypes(alleles) dictating the effectiveness of these drugs.

Today, we are struggling to fully comprehend the effects of these known gene combinations, let alone those of the numerous rare haplotypes formed through mutations (SNPs) found across the world further convoluted by, ironically, the uniformity of variation across ethnicities. it is however important as predicting the functions of these novel alleles is key to improving the drug responses of these patients.

It is at this juncture that deep learning becomes relevant as the next machine learning paradigm. By implementing techniques that combine CNN image analysis and transfer learning, we can build deep neural networks that can generate voluminous sequences with known variations spiked into these sequences. Once we generate scores for each allele, we can train a model to assign these scores, forcing the CNN to ‘learn’ key sequence features. The experimental data on rare variations can be used to refine the final layers of the network, which can then predict and assess the outcomes of using codeine in individuals with rare alleles of CYP2D6.

Looking ahead

One must not overlook the fact that ML in genomics is in its infancy; a field that is less than a decade old. The reason is that the 3-D relationships shared by genes are much more complex than pixels and their interactions; as mentioned earlier, image recognition and analysis have great potential in the ML world. Today, breakthroughs in research have led to the union of both these techniques: ML devices like Deep Gestalt can accurately diagnose( up to 91%) over 200 disorders through image analyses by observing facial phenotypic manifestations of genetic aberration(note that the utilization AI/ML in genomics corresponds to deep learning and that remains so for the scope of this discussion, as well).

Deep learning neural networks are of 4 types broadly and they use different inputs; while fully connected networks use k-mer match metrics, convolutional and recurrent networks can use DNA sequence data, as well as image and time-series measurements respectively. The fourth type, graph convolutional, utilizes protein-to-protein interaction and structure. These are often manifest as modalities that can identify sequence context features capable of predicting transcription factor binding, decoded networks revealing differential gene expression, and more.

Applying Deep Learning to arrive at this profound understanding of gene mutations can be pivotal in understanding the origins of tumors and the development of gene therapy for cancer prevention and the prevention and treatment of many genetic disorders. It can cut down on years of painstakingly slow research and help us arrive at possible solutions much faster, or it can help us choose the most promising solutions – potentially saving us millions of dollars that could be wasted by going down the wrong road.

Yet, these technologies have miles to go before achieving mainstream acclaim and widespread professional implementation; for now, scientific incubation in research labs under the eyes of experts is the best mode of development for predictive genomics.

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Blockchain:  

While bitcoin has always been a headliner, few recognize its driver technology known as Blockchain. It is an advanced technology utilizing a peer-to-peer ledger of records that is of virtually incorruptible safety. As it is designed to be self-managed, it is highly affordable, barring the initial capital requirement. It can tread great lengths in fixing the apparent intangibility customers feel towards their insurance plans and policies. Due to its unique and secure trademarks, it could increase processing speed, help establish customer trust, and expedite claim processing without falling prey to fraudulence and manipulation.  

Artificial Intelligence: 

While AI needs no introduction, its deployment in InsurTech brings to the table a host of unprecedented gains that can revolutionize the face of the financial domain. Moreover, with personalization now being the norm for everything, AI has much to offer in creating bespoke experiences, helping customers feel discerning, and bolstering their trust.  

With adoption levels and implementation still slow, experts believe that its initial impact will be in the automation of underwriting and claims processes. In tandem with machine learning, it will analyze risks and identify new sources of capital that could lead to new frontiers in checking fraud and money laundering.  

Gamification:  

This is undoubtedly one of the most peculiar and yet promising trends of InsurTech in 2021, showcasing the adoption of video-game-inspired strategies like level clearing and unlocking, performance bonuses, and so on. Although rare, some actuarial companies even deploy their games to promote their insurance-related products. It may also lead to a sense of brand loyalty to the firm.  

Younger clients often duck the insurance business due to its labyrinthine and precarious nature; with this customer category, gamification has yielded the most results. Moreover, firms can utilize the aspects of gamification at every juncture in the customer experience, unlike the other trends that limit themselves to technological aspects.  

Machine Learning: 

Although machine learning is related to AI (while being highly specific), it is only with its integration that InsurTech can achieve total efficiency. While artificial intelligence can improve and speed up claims processing, it is only with machine learning that we can automate it. The power of pre-programmed algorithms will be a great tool in automation using the digital files accessible via the cloud. And its utility is not limited to either P&C firms or their customers; it supports both entities almost equally. In addition, it has numerous other payoffs, like risk calculation, CLV, and PIE computation.  

IoT: 

With the ever-rising number of connected devices around the world, insurers can use these tools to harness data; the open-source data available from smartwatches, homes, and even automobile sensors can help collect pivotal data that can assess the customer psyche. A simple use case is the collection of health data from a client’s fitness smartwatch, which could furnish the P&C insurers with information that would help in policymaking for the said client. Though it has come under the scanner for potential infringements of customer privacy and corporate fraud, this trend continues to thrive and grow with some of the top insurance companies in the US today.   

If you are looking for a Digital Product Engineering Services partner who not only keeps up with emerging trends but takes giant strides in the creation of their own, blackrock is the right partner for you. Browse our website to see more on our FinTech products.  

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Like many other industries, supply chains, which are the backbone of national economies, also had to change their strategies to stay efficient and meet the new market requirements. As a result, they have embraced digital solutions to cope with our current times’ uncertainties, challenges, and restrictions.  

The supply chain ecosystem became all the more critical once the vaccines for COVID-19 were developed – it was the need of the hour to get the vaccines to the public quickly and in the right quantities.    

Challenges and roadblocks in the COVID-19 vaccine supply chain    

The biggest problem in any supply chain management is matching demand & supply efficiently.  

Supply chain management companies are currently facing issues due to the disparity in demand and supply for COVID-19 vaccines from region to region. In addition, the lack of raw material and human resources has caused delays in the supply of COVID-19 vaccines.  

Inequalities regarding vaccine distribution have become a global problem, as well. For example, reports show that financially stable countries are getting vaccinated 30 times more than those with lower incomes.   

Vaccine management issues such as storage capacity, handling management, effective distribution between supply chain levels, and shipment procedure are also of rising concern as two doses are required for most vaccines.    

Security issues such as theft and mishandling are other grave concerns, especially considering the demand for COVID-19 vaccines is higher than the supply. Time delay and lack of visibility into supply are also issues researchers have noted.   

Digital solutions reshaping Supply Chain Management  

Digital solutions have played a crucial role in helping retailers, suppliers, and distributors drive the transformational changes required to address the challenges posed. Therefore, the accelerated adoption of these solutions in supply chain models is essential not only for the present but also for the future.  

Here are some of the ways digital solutions have made an impact on supply chain ecosystems:   

Machine Learning: 

Shifting supply chain dynamics, changing ways of working, and increasingly volatile demand has been a concern for suppliers, distributors, manufacturers, and retailers globally when it came to an efficient distribution of Covid-19 vaccines.  

McKinsey predicts that machine learning’s most significant contributions will be providing supply chain operators with significant prescriptive insights into how supply chain performance can be improved by anticipating anomalies in logistics costs and performances before they occur. In addition, machine learning models and techniques can ensure streamlined production planning, inventory management, and anomaly detection and can offer an exceptional customer experience.  

Artificial Intelligence: 

AI-based tools can help understand which geographic regions to target for vaccine supply to flatten the curve of the pandemic sooner, provide insights in customizing the supply chain management system to ensure maximum vaccination in the least amount of time, and ensure the processes are being followed as designed.  

Artificial intelligence tools can also be leveraged for capacity planning, predicting the demand for raw materials, work-in-progress components, and post-vaccination surveillance. With AI, supply chain management companies can improve responsiveness to vaccine demand, minimize risk, and increase visibility & transparency across the supply chain.  

Data Analytics: 

The race to vaccinate the global population is a daunting task and needs data-driven strategies and action plans to optimize the supply chain. Data analytics tools capture inventory, demand, capacity, and other related data across the distribution chain, to create a strong distribution strategy to help supply chain management companies handle the fluctuating demand and supply.  

Data analytics tools like predictive analytics have also helped distribution companies predict vaccine demand in any geography and streamline production and distribution accordingly. In addition, using advanced data analytics technologies, governments can identify and create priority populations in different geographic locations and formulate a vaccination policy that maximizes vaccination rates and minimizes wasted dosages.  

IoT: 

IoT sensors are used to keep track of the temperature in storage facilities and vehicles during transportation. Armed with real-time alerts, IoT solutions let stakeholders be aware of any system failures & let them monitor and optimize the vehicle routes. IoT systems can also track vaccine stocks in financially stable countries to ensure a smoother redistribution of any surplus vaccines to developing countries across the globe.  

Blockchain: 

Blockchain-based solutions have been recognized as the backbone in developing a reliable and transparent supply chain management system to manage COVID-19 vaccine rollouts.  

With its unique capabilities, Blockchain technology can help supply chain companies to track the transportation and storage of vaccine batches in real-time, verify vaccines’ provenance and authenticity, quick detection and identification of faulty products, and identifying and blocking counterfeit vaccines from entering the supply chain. Blockchain-based solutions ensure accurate traceability, enhanced security, and greater transparency in vaccine distribution. 

In Conclusion   
COVID-19 has revealed the fragility of existing supply chain management systems and has proved the potential of digital solutions to cope with unprecedented disruption effectively and efficiently. Advancements in digital technologies such as machine learning, the internet of things (IoT), blockchain, artificial intelligence (AI), and data analytics will pave the way for agile, reliable, and efficient supply chain management systems capable of handling dynamic supply and demand.  

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Resume parsing was introduced to simplify the recruitment process using automation, by leveraging Machine Learning & Natural Language Processing. It helps HR professionals intelligently manage resume information, by removing the headache of manually handling each resume. Incorporating resume parsing helped enterprises streamline the entire recruitment process, helping them hire the right talent for the right job efficiently.

What is resume parsing?

Resume parsing is a technology that enables automatic processing of resumes by extracting available data from resumes and organizing in a structured manner. In addition to extracting standard fields such as name, work experience, email, contact number, education skills, etc., custom fields can be added to include other information that might not have included in the traditional resume format.

Why Resume Auto Parser (RAP)?

Combing through resumes in search of specific requirements covering multiple parameters has always been a nightmare for recruiters. This becomes additionally inefficient as the experience level, and skills of candidates keep improving, and there are only few tools with which recruiters can keep track of these dynamics without the active involvement of candidates.

As part of our recent engagement with a large enterprise, blackrock developed a customized version of Resume Parser solution which helps the client’s HR department add or update the candidate profile automatically whenever a candidate presents a new or an updated resume.

How does the Resume Auto Parser (RAP) Solution work?

The RAP solution is based on pattern recognition, encapsulating the patterns commonly observed in resumes of technical professionals, especially in the IT domain. The pilot version of the solution developed by blackrock can capture the following candidate information from a large repository of resumes:

1) Name 2) Telephone number 3) Email 4) Years of experience 5) Technical skills 6) Languages spoken 7) Hobbies, etc.

The solution can examine any number of resumes and generate a report with the above details. The input file format can be DOC, PDF, or JPEG.  The output format is CSV, which enables the report to be viewed and formatted using MS Excel.

Challenges Identified

1) The diversity of resume design and file-formats coupled with lack of annotated data means that any data-intensive approaches become impractical.

2) It has been observed in various instances that name (candidate names) recognition using tools such as SPACY was not yielding accurate results as the model (‘en_core_web_sm’) did not prove to be accurate in identifying Indian names and surnames. An attempt to train the model using an available dataset of Indian names, though improved the performance, also fell short of desired accuracy level.

Document Parsing

Resumes are saved mainly in DOC or PDF format. Rarely, resumes could have an image format as well, especially while dealing with screen-prints. The RAP solution is designed for parsing resumes in DOC, PDF, JPG and PNG formats.

RAP Architecture

The overall architecture of Experion’s RAP solution combines pattern recognition, expert systems, and regular expressions to undertake intensive text analytics.

a) Pattern Recognition

Pattern recognition is used for capturing the name of a candidate. A pattern for capturing candidate names is identified after observing a considerable number of resumes. This pattern can identify names to a large extent accurately. Similarly, another pattern was identified and followed for isolating the overall work experience.

b) Expert System

An expert system is used on a smaller scale, mostly in isolating the text for fetching the candidate names and overall years of experience.

c) Regular Expression

Python regular expression (Regex) is used for extracting the email ids and contact numbers of the candidates. A certain level of formatting is also performed over the captured phone numbers so that they appear in a consistent format.

For example:

• 1234567890 is transformed to 123-456-7890
• 91 1234567890 is transformed to +91-123-456-7890 etc

The RAP solution is also capable of extracting more than one contact number and email ids.

How did  Experion’s Resume Auto Parser (RAP) solution benefit recruiters?

• Examine any number of resumes to extract and summarize required information in easily readable tabular format.
• Handle most file formats such as DOC PDF, JPG, PNG, etc.
• Fetch candidate information from screenshots.
• Provide information in sorted order of overall years of experience, which enables recruiters to map their requirements with a profile accurately.

Areas of improvement

The pattern recognition, though displays appreciable performance, still needs fine-tuning to be perfect. There are rare cases in which the expert system fails to isolate the text while recognizing a candidate name.

Reference & Courtesy

https://github.com/bjherger/ResumeParser

Our code is a profoundly improved/ refurbished version of the above.

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