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A loan passes through various stages or events from the moment it is given till the time it is repaid. Collection strategy of a loan for any financial institution is as important as its lending strategy and delays in repayments not only impacts the financer’s books, it also impacts the borrower as it is also reflected in their credit history SHG / JLG Collections A SHG (self help group) is a community based group with 5-20 members. Micro Finance Institutions typically offer group loans and individual loans that have standardized repayment structure. The repayment cycle could be weekly, monthly or fortnightly depending on the scheme and institutions. In a typical collection process either an MFI agent visits the borrower to collect the repayment in cash or the borrower walks to a physical branch to make the payments. How Data Science Helps Predictive analytics plays a key role in coming up with behavioral patterns to determine whether a customer is likely to default. A right collection model can be a driving factor behind a product’s collection efficiency. Simple classification model or a scorecard can be trained on the past data to help the collection team to identify the chunk of customers in the current portfolio who display a similar pattern to the ones who defaulted in the same product in the past. It can help the collection team to put more focus on these customers and align their efforts accordingly. This model will run at the start of every collection cycle and its frequency will be similar to the repayment frequency of the customer(weekly, monthly or fortnightly). The two major category of variables that can be used to identify this pattern are:   Credit Bureau Data Looking at customer’s credit data tells us the customer’s current market activity and his/her past credit history. Major variables that can help us identify our customer’s potential credit default include: Customer’s Internal Performance Data You also have repayment history of the customer with you. It can also be broken down into three types of variables Machine learning algorithms are fed all of this data from which they learn and create predictions. These algorithms can extract linear and nonlinear patterns in the data which will be difficult for a human(Collection team) to see. A multivariate machine learning model with hundreds of features can easily outperform a univariate rule based collection strategy. Use of applied machine learning can not only give you better results but also a clear interpretability and deeper insights for business to make better decisions. With the help of predictive analytics in collections MFIs can maintain good clean books and can aim to achieve higher profitability.

For oil and gas businesses operating at the highest levels of efficiency while keeping costs in control and increasing productivity is a challenging task. Since oil and gas organisations are asset rich in nature, equipment safety and reliability becomes significantly important. To limit downtime and minimize risks, oil and gas companies are leveraging industrial data and advanced analytics. This helps in predictive maintenance execution consequently empowering people to act before equipment failure occurs. 1) Smarter Maintenance: Oil and gas operations require a diverse set of complex and critical assets throughout the upstream, midstream and downstream processes. These assets include offshore pumping station, compressor, drilling rig, transportation equipment, pipeline booster station etc. Monitoring health and performance of such assets presents a substantial challenge when oil and gas operations are remotely located. Real-time asset health data and performance insights can be used to take informed decisions that drive efficiencies, mitigate risks and improve competitive advantage. Reactive Maintenance (RM): This is the most basic approach which involves letting an asset run until failure. It is suitable for non-critical assets that have little to no immediate impact on safety and have minimal repair or replacement costs so that they do not warrant an investment in advanced technology. Preventative Maintenance (PM): This approach is implemented in hopes that an asset will not reach the point of failure. The preventative maintenance strategy can be formulated on a: fixed time schedule or operational statistics and manufacturer/ industry recommendations of good practice. Preventative maintenance can be managed in the Enterprise Asset Management (EAM) or Computerized Maintenance Management System (CMMS). Condition-Based Maintenance (CBM): CBM is a proactive approach that focuses on the physical condition of equipment and how it is operating. CBM is ideal when measurable parameters are good indicators of impending problems. CBM follows rule-based logic where rule defines a certain condition and these rules do not change depending on loading, ambient or operational conditions. Predictive Maintenance (PdM): Predictive maintenance is implemented for more complex and critical assets. It relies on the continuous monitoring of asset performance through sensor data and prediction engines to provide advanced warning of equipment problems and failures. PdM typically uses Advanced Pattern Recognition (APR) and requires a predictive analytics solution for real-time insights of equipment health. Risk-Based Maintenance (RBM): RBM enables comprehensive decision making to plant operations and maintenance personnel using PdM, CBM and PM outcomes. This leads to a reliable and safe planning for maintenance and the operation of equipment or assets. 2) Predictive Analytics Predictive analytics together with PdM can lead to the identification of issues that may not have been found otherwise. According to research by ARC Advisory Group, only 18 percent of asset failures had a pattern that increased with use or age (Rio, 2015). This means that preventive maintenance alone is not sufficient to avoid the other 82 percent of asset failures, and a more advanced approach is required. Predictive analytics software keeps a track of historical operational signatures of each asset and compares it to real-time operating data to detect even the precise changes in equipment behavior. This approach helps in taking corrective actions by identifying changes in system behavior well before traditional operational alarms. 3) Health and Performance Optimization With Predictive asset analytics software solutions, oil and gas organizations get early warning notifications of equipment issues and potential failures which help them to take corrective measures and improve overall performance. How Predictive Asset Analytics software solutions work? The software learns an asset’s unique operating profile during all loading, ambient and operational conditions through the advanced modeling process. The result of the modeling process is a unique asset signature that is compared to real-time. This comparison uses operating data to determine and alert upon detection of subtle deviations from expected equipment behavior before they become problems that significantly impact operations. Such software are able to identify problems days, weeks or months before they occur and provide early warning notifications of developing issues. Together, this helps the plant and operations personnel to be proactive to reduce unscheduled downtime. This proactive approach leads to better planning and helps in reducing maintenance costs as parts can be ordered and shipped without rush and equipment can continue running. Other benefits include increased asset utilisation and the ability to identify under performing assets. Not only do companies improve their profitability by extending equipment life, lengthening maintenance windows, and increasing asset availability, other benefits are realised when considering the costs that “could have been,” including replacement equipment, lost productivity, additional man hours, etc., when a major failure is avoided. Another increasingly important benefit is the capability for knowledge capture and transfer. Predictive asset analytics solutions ensure that maintenance decisions and processes are repeatable even when organizations are faced with transitioning workforces, and the loss of experienced workers with critical institutional knowledge of the operations and maintenance of the organisation’s facilities. 4) Smarter Operations Internet of things has potential to create tremendous business value by enabling smarter equipment integration that creates increasing amounts of data. Oil and gas companies are faced with both challenges and opportunities to leverage that data to mitigate risk and improve productivity. With the help of predictive analytics, they can ascertain and comprehend actual and expected performance for an asset’s current ambient, loading and operating conditions. This information helps enterprises in: Source: http://software.schneider-electric.com/pdf/industry-solution/predictive-analytics-for-improved-performance-in-oil-and-gas/

The Financial Services Industry has went through a complete transformation at its very core. While the good old days were marked by a few Giant corporations handling the whole industry, today’s age of agile startups has seen an influx of various small and medium FinTech companies that are lean and efficient – both in their operations as well as the customer experience. The Customers too, habituated to the relative ease of other services that underwent digital transformation, like calling a taxi or finding a hotel, expect much. Their demands are ever increasing and the cost of failure has become too high. So what is a guy supposed to do to keep up? Simple. Make things personal   Technology Has Made Things Personal Much Easier New tech like marketing cloud, big data and intelligent technologies such as artificial intelligence (AI) & Deep Learning (DL) are allowing companies to do much more than what they were previously capable of. They are used for developing and deploying sophisticated algorithms to make sure that customers are shown things they want to be shown. However, the Financial Services industry has been a bit late to the party. A recent Digital Banking Report found that “roughly 40 percent of all but the very largest financial institutions consider themselves ‘static,’ meaning they offer no personalization within their application”. That’s not cool. Not at all. It’s a pretty well known fact that customer experience (CX) through personalization drives more revenue and improves loyalty, affinity and lifetime value, which should all appeal to the money-conscious cohort that Financial Services is. Think Across Multiple Channels – Work For The Same Goal Poll a room full of individuals on whether they’ve visited a physical bank location in the last month and you’re likely to get only a small show of hands. Yet, a study found that the average customers have 10 digital interactions per month with their main bank. While interactions are happening less frequently at physical locations, banks can still have meaningful interactions with customers if the organization can move beyond thinking in channels. Most organizations are marketing to their target audience based on some basic principles and rules outlined per channel. In any case, if the bank’s activities are not closely monitored across various channels, they lose track of a customer when they moves from one channel onto the next—say from mobile to PC. Without a top level view of the customer’s journey, it’s pretty hard to deliver them an experience that seems both personal as well as seamless across multiple channels. All in all, what’s acting as a burden? Two things: separated information and the absence of a strategic viewpoint that covers the whole journey of the customer. Genuine personalization takes in constant behavioral information from everywhere – web browsing sessions, IoT In any case, to see every client’s journey, Financial Services establishments need to incorporate old as well as transactional information secured in Kiosks (like billied amount,etc.), and also event information, for example, missed installments. By joining information (behavioral, value-based and verifiable) into a solitary perspective of the customer, the bank can boost its conversion by being constantly present on all platforms and delivering a message tailored to both the customer as well as the platform. How To Scale Personalization In Financial Services – The Artificial Intelligence Way The National Business Research Institute surveyed 100 financial services executives and found that only 32 percent of the group were using AI technologies such as predictive analytics, image processing, recommendation engines, voice recognition, and response. Even amongst these 32 percent, most executives work for global level organizations. The main reason for this lies in the fact that there is a big absence of Top level Technocrats in these organizations. Most of their tech solutions are outsourced to agencies which have no idea how to build software that are smart. Personalization is essential for any organization of any scale – it’s direct impact on revenue is too positive to ignore. Traditionally,Banks have been putting  their customers in segments: They divide customers into groups and tailor offers and communications accordingly. Slicing customers into these segments with modern marketing tools makes them progressively smaller until the number explodes and becomes too complex to manage. Once this segment gets down to 1:1 , a phenomenon called “audience explosion” happens. The phenomenon is easy to understand (but tremendously hard to handle, at least manually). It become simply just too difficult to offer personalized recommendations to millions of customers. Even simple automation won’t suffice here which is why AI is used to serve each customer a platter of stuff they love the most. Some banks have already started to invest in virtual assistants to interact with their customers via chatbots, which predict and react to changes in customer behavior with AI. The power to serve every customer in this way across every channel gives banks unlimited potential to grow their business. Summing It Up Artificial Intelligence is a force – much akin to electricity. How you are using it is entirely upto you. But the one thing that is sure is that it WILL make your work both better as well as easier. Banks are just one of the many industries that have yet to understand the plethora of benefits AI is going to be bringing. Personalization has been a proven method of both incrementing revenue as well as making customers happy – and AI, is what is going to ensure that all Banks are doing personalization right.