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3 Ways to Close the Data Gaps in Commercial Banking

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Click to learn more about author Clayton Weir.

Over the last few years, retail banking has done a tremendous job of making the user experience sleeker and more frictionless. Yet, for all of the great strides that have been made in revolutionizing the retail banking experience – both on the front- and back-end – the commercial banking experience still has significant room for growth.

Digital transformation efforts during the COVID-19 pandemic have completely changed the way banks operate today. But when it comes to the commercial side of their businesses, many banks still rely on archaic methods and tools. Take data exchange, for example. Instead of offering sophisticated APIs to meet the data exchange needs of their customers, many commercial banks still rely on mass Excel data file dumps as the primary way to deliver data to and from customers. What’s more, once a client receives this data, the onus is on them to put it into an acceptable format for upload into their ERP. This has obviously gotten tiresome for commercial banking clients who have become accustomed to the ease of the retail banking experience.

With that in mind, banks are scrambling to close the gap between their retail and commercial banking experiences as competition for customers remains at an all-time high. And one of the foremost areas they are trying to improve is their data operations.

Here are a few ways that banks can improve their data operations and deliver the next-level experiences that their business customers seek.

1. Embrace Open Data and Banking

From enabling improved customer experiences to opening new revenue streams, the potential for open data in banking is massive. However, at the current moment we have really only seen the tip of the iceberg when it comes to the power of open banking – especially in the U.S. Granted, switching to an open banking model may seem like a formidable task for many banks. However, thanks to the groundswell of innovation in the banking and financial technology space today, adopting modern methods and tools has never been easier. Additionally, once banks have transitioned to a more open model, it will become easier for their entire business to become more agile and responsive.

2. Rethink “Building It Yourself”

When it comes to technology, banks continue to stumble when it comes to building their own technology. Manufacturing APIs and other tools can be both a time- and capital-intensive process. Additionally, given how rapidly customer expectations are changing in the commercial banking space today, self-built tools are often outdated by the time they hit the market. Therefore, banks need to begin thinking outside of their organization for technology options that can deliver the solutions they need today and tomorrow. If not, banks will likely be stuck playing catch-up instead of beating out their competitors.

3. Adopt Embedded Banking

The commercial banking experience today is laden with a seemingly endless number of hoops that business customers have to jump through to accomplish their day-to-day tasks. But what if all of these processes could be consolidated and integrated natively into the platforms business customers already use every day? They can: Through the power of embedded banking, banks can unlock a much more streamlined and efficient end-to-end experience for their customers. More importantly, by adopting embedded banking, banks can eliminate the need for additional manual legwork, meaning their workforces can focus more squarely on business growth versus busy work.

Conclusion

The digital banking revolution is not going to slow down anytime soon. And as consumers become more and more accustomed to the seamless world of retail banking, calls for improvements in commercial banking are only going to grow. And while this may seem like a challenging undertaking, by adopting new data handling tools and processes, banks can make significant customer experience gains in a very short space of time. Moreover, by modernizing their data ops, banks can better position themselves for the challenges of both today and tomorrow.

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
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Source: https://www.dataversity.net/3-ways-to-close-the-data-gaps-in-commercial-banking/

Big Data

If you did not already know

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DataOps google
DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations. From a process and methodology perspective, DataOps applies Agile software development, DevOps software development practices and the statistical process control used in lean manufacturing, to data analytics. In DataOps, development of new analytics is streamlined using Agile software development, an iterative project management methodology that replaces the traditional Waterfall sequential methodology. Studies show that software development projects complete significantly faster and with far fewer defects when Agile Development is used. The Agile methodology is particularly effective in environments where requirements are quickly evolving – a situation well known to data analytics professionals. DevOps focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of analytics. This merging of software development and IT operations has improved velocity, quality, predictability and scale of software engineering and deployment. Borrowing methods from DevOps, DataOps seeks to bring these same improvements to data analytics. Like lean manufacturing, DataOps utilizes statistical process control (SPC) to monitor and control the data analytics pipeline. With SPC in place, the data flowing through an operational system is constantly monitored and verified to be working. If an anomaly occurs, the data analytics team can be notified through an automated alert. DataOps is not tied to a particular technology, architecture, tool, language or framework. Tools that support DataOps promote collaboration, orchestration, agility, quality, security, access and ease of use. …

CoSegNet google
We introduce CoSegNet, a deep neural network architecture for co-segmentation of a set of 3D shapes represented as point clouds. CoSegNet takes as input a set of unsegmented shapes, proposes per-shape parts, and then jointly optimizes the part labelings across the set subjected to a novel group consistency loss expressed via matrix rank estimates. The proposals are refined in each iteration by an auxiliary network that acts as a weak regularizing prior, pre-trained to denoise noisy, unlabeled parts from a large collection of segmented 3D shapes, where the part compositions within the same object category can be highly inconsistent. The output is a consistent part labeling for the input set, with each shape segmented into up to K (a user-specified hyperparameter) parts. The overall pipeline is thus weakly supervised, producing consistent segmentations tailored to the test set, without consistent ground-truth segmentations. We show qualitative and quantitative results from CoSegNet and evaluate it via ablation studies and comparisons to state-of-the-art co-segmentation methods. …

Stochastic Computation Graph (SCG) google
Stochastic computation graphs are directed acyclic graphs that encode the dependency structure of computation to be performed. The graphical notation generalizes directed graphical models. …

Smooth Density Spatial Quantile Regression google
We derive the properties and demonstrate the desirability of a model-based method for estimating the spatially-varying effects of covariates on the quantile function. By modeling the quantile function as a combination of I-spline basis functions and Pareto tail distributions, we allow for flexible parametric modeling of the extremes while preserving non-parametric flexibility in the center of the distribution. We further establish that the model guarantees the desired degree of differentiability in the density function and enables the estimation of non-stationary covariance functions dependent on the predictors. We demonstrate through a simulation study that the proposed method produces more efficient estimates of the effects of predictors than other methods, particularly in distributions with heavy tails. To illustrate the utility of the model we apply it to measurements of benzene collected around an oil refinery to determine the effect of an emission source within the refinery on the distribution of the fence line measurements. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/10/24/if-you-did-not-already-know-1540/

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Big Data

If you did not already know

Published

on

DataOps google
DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations. From a process and methodology perspective, DataOps applies Agile software development, DevOps software development practices and the statistical process control used in lean manufacturing, to data analytics. In DataOps, development of new analytics is streamlined using Agile software development, an iterative project management methodology that replaces the traditional Waterfall sequential methodology. Studies show that software development projects complete significantly faster and with far fewer defects when Agile Development is used. The Agile methodology is particularly effective in environments where requirements are quickly evolving – a situation well known to data analytics professionals. DevOps focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of analytics. This merging of software development and IT operations has improved velocity, quality, predictability and scale of software engineering and deployment. Borrowing methods from DevOps, DataOps seeks to bring these same improvements to data analytics. Like lean manufacturing, DataOps utilizes statistical process control (SPC) to monitor and control the data analytics pipeline. With SPC in place, the data flowing through an operational system is constantly monitored and verified to be working. If an anomaly occurs, the data analytics team can be notified through an automated alert. DataOps is not tied to a particular technology, architecture, tool, language or framework. Tools that support DataOps promote collaboration, orchestration, agility, quality, security, access and ease of use. …

CoSegNet google
We introduce CoSegNet, a deep neural network architecture for co-segmentation of a set of 3D shapes represented as point clouds. CoSegNet takes as input a set of unsegmented shapes, proposes per-shape parts, and then jointly optimizes the part labelings across the set subjected to a novel group consistency loss expressed via matrix rank estimates. The proposals are refined in each iteration by an auxiliary network that acts as a weak regularizing prior, pre-trained to denoise noisy, unlabeled parts from a large collection of segmented 3D shapes, where the part compositions within the same object category can be highly inconsistent. The output is a consistent part labeling for the input set, with each shape segmented into up to K (a user-specified hyperparameter) parts. The overall pipeline is thus weakly supervised, producing consistent segmentations tailored to the test set, without consistent ground-truth segmentations. We show qualitative and quantitative results from CoSegNet and evaluate it via ablation studies and comparisons to state-of-the-art co-segmentation methods. …

Stochastic Computation Graph (SCG) google
Stochastic computation graphs are directed acyclic graphs that encode the dependency structure of computation to be performed. The graphical notation generalizes directed graphical models. …

Smooth Density Spatial Quantile Regression google
We derive the properties and demonstrate the desirability of a model-based method for estimating the spatially-varying effects of covariates on the quantile function. By modeling the quantile function as a combination of I-spline basis functions and Pareto tail distributions, we allow for flexible parametric modeling of the extremes while preserving non-parametric flexibility in the center of the distribution. We further establish that the model guarantees the desired degree of differentiability in the density function and enables the estimation of non-stationary covariance functions dependent on the predictors. We demonstrate through a simulation study that the proposed method produces more efficient estimates of the effects of predictors than other methods, particularly in distributions with heavy tails. To illustrate the utility of the model we apply it to measurements of benzene collected around an oil refinery to determine the effect of an emission source within the refinery on the distribution of the fence line measurements. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/10/24/if-you-did-not-already-know-1540/

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Big Data

If you did not already know

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Correntropy google
Correntropy is a nonlinear similarity measure between two random variables.
Learning with the Maximum Correntropy Criterion Induced Losses for Regression


Patient Event Graph (PatientEG) google
Medical activities, such as diagnoses, medicine treatments, and laboratory tests, as well as temporal relations between these activities are the basic concepts in clinical research. However, existing relational data model on electronic medical records (EMRs) lacks explicit and accurate semantic definitions of these concepts. It leads to the inconvenience of query construction and the inefficiency of query execution where multi-table join queries are frequently required. In this paper, we propose a patient event graph (PatientEG) model to capture the characteristics of EMRs. We respectively define five types of medical entities, five types of medical events and five types of temporal relations. Based on the proposed model, we also construct a PatientEG dataset with 191,294 events, 3,429 distinct entities, and 545,993 temporal relations using EMRs from Shanghai Shuguang hospital. To help to normalize entity values which contain synonyms, hyponymies, and abbreviations, we link them with the Chinese biomedical knowledge graph. With the help of PatientEG dataset, we are able to conveniently perform complex queries for clinical research such as auxiliary diagnosis and therapeutic effectiveness analysis. In addition, we provide a SPARQL endpoint to access PatientEG dataset and the dataset is also publicly available online. Also, we list several illustrative SPARQL queries on our website. …

LogitBoost Autoregressive Networks google
Multivariate binary distributions can be decomposed into products of univariate conditional distributions. Recently popular approaches have modeled these conditionals through neural networks with sophisticated weight-sharing structures. It is shown that state-of-the-art performance on several standard benchmark datasets can actually be achieved by training separate probability estimators for each dimension. In that case, model training can be trivially parallelized over data dimensions. On the other hand, complexity control has to be performed for each learned conditional distribution. Three possible methods are considered and experimentally compared. The estimator that is employed for each conditional is LogitBoost. Similarities and differences between the proposed approach and autoregressive models based on neural networks are discussed in detail. …

Discretification google
Discretification’ is the mechanism of making continuous data discrete. If you really grasp the concept, you may be thinking ‘Wait a minute, the type of data we are collecting is discrete in and of itself! Data can EITHER be discrete OR continuous, it can’t be both!’ You would be correct. But what if we manually selected values along that continuous measurement, and declared them to be in a specific category? For instance, if we declare 72.0 degrees and greater to be ‘Hot’, 35.0-71.9 degrees to be ‘Moderate’, and anything lower than 35.0 degrees to be ‘Cold’, we have ‘discretified’ temperature! Our readings that were once continuous now fit into distinct categories. So, where we do we draw the boundaries for these categories? What makes 35.0 degrees ‘Cold’ and 35.1 degrees ‘Moderate’? At is at this juncture that the TRUE decision is being made. The beauty of approaching the challenge in this manner is that it is data-centric, not concept-centric. Let’s walk through our marketing example first without using discretification, then with it. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/10/23/if-you-did-not-already-know-1539/

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Big Data

Capturing the signal of weak electricigens: a worthy endeavour

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Recently several non-traditional electroactive microorganisms have been discovered. These can be considered weak electricigens; microorganisms that typically rely on soluble electron acceptors and donors in their lifecycle but are also capable of extracellular electron transfer (EET), resulting in either a low, unreliable, or otherwise unexpected current. These unanticipated electroactive microorganisms represent a new chapter in electromicrobiology and have important medical, environmental, and biotechnological relevance.
PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00229-8?rss=yes

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