AC3 Achieves Significant Efficiency Gains Through Automation and Data Enrichment Using the Aunalytics Daybreak Solution
AC3 Achieves Significant Efficiency Gains Through Automation and Data Enrichment Using the Aunalytics Daybreak Solution
With Aunalytics, AC3 automates billing processes and provides revenue cycle insights to oncology providers for greater visibility into missing revenue opportunities.
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Aunalytics is a data platform company. We deliver insights as a service to answer your most important IT and business questions.
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2021 Missouri Bankers Association Annual Convention
Missouri Bankers Association Annual Convention & Trade Show
Chateau on the Lake Resort Spa & Convention Center, Branson, MO
Aunalytics is attending the 2021 MBA Annual Convention as a Gold Sponsor
Aunalytics, a leading data platform company delivering Insights-as-a-Service for enterprise businesses, will join the upcoming Missouri Bankers Association Annual Convention as a Gold Sponsor, July 13-16 in Branson, MO. Aunalytics will feature its Daybreak™ for Financial Services solution with advanced analytics that enable bankers to more effectively identify and deliver new services and solutions for their customers for greater competitive advantage.
What are Smart Features?
Machine learning is a leading component of today’s business landscape, but even many forward-looking business leaders in the mid-market have difficulty developing a strategy to leverage these cutting edge techniques for their business. At Aunalytics, our mission is to improve the lives and businesses of others through technology, and we believe that what many organizations need to succeed in today’s climate is access to machine learning technology to enhance the ability to make data driven-decisions from complex data sources.
Imagine having the ability to look at a particular customer and understand based on past data how that individual compares in terms of various factors driving that business relationship:
- Which of our products is this customer most likely to choose next?
- How likely is this customer to default or become past due on an invoice?
- What is churn likelihood for this customer?
- What is the probable lifetime value of this customer relationship?
Aunalytics’ Innovation Lab data scientists have combed through data from our clients in industries like financial services, healthcare, retail, and manufacturing and have developed proprietary machine learning techniques based on a solid understanding of the data commonly collected by businesses in these sectors.
A SHAP (Shapley Additive Explanations) value chart for a remarkably accurate loan default risk model we developed shows which features have the highest impact on risk prediction.
From this, we append insights gleaned from machine learning to data models. We add high value fields to customer records to reveal insights about a customer learned from our algorithms. Smart Features provide answers to pressing business questions to recommend next steps to take with a particular customer to deepen relationships, provide targeted land and expand sales strategies, provide targeted marketing campaigns for better customer experiences, and yield business outcomes.
Machine learning techniques enable more accurate models of risk, propensity, and customer churn because they represent a more complex model of the various factors that go into risk modeling. Our models deliver greater accuracy than simpler, statistical models because they understand the relationship between multiple indicators.
Smart Features are one way that Aunalytics provides value to our clients by lending our extensive data science expertise to client-specific questions. Through these machine learning enriched data points, clients can easily understand a particular customer or product by comparing it to other customers with similar data. Whether you want to know if a customer is likely to select a new product, their default risk, churn likelihood, or any other number of questions, our data scientists and business analysts are experienced and committed to answering these questions based on years of experience with businesses in your industry.
Where Can I Find an End-to-End Data Analytics Solution?
The data analytics landscape has exploded over the past decade with an ever-growing selection of products and services: literally thousands of tools exist to help business deploy and manage data lakes, ETL and ELT, machine learning, and business intelligence. With so many tools to piece together, how do business leaders find the best one or ones? How do you piece them together and use them to get business outcomes? The truth is that many tools are built for data scientists, data engineers and other users with technical expertise. With most tools, if you do not have a data science department, your company is at risk for buying technologies that your team does not have the expertise to use and maintain. This turns digital transformation into a cost center instead of sparking data driven revenue growth.
Image credit: Firstmark
https://venturebeat.com/2020/10/21/the-2020-data-and-ai-landscape/
Aunalytics’ side-by-side service model provides value that goes beyond most other tools and platforms on the market by providing a data platform with built-in data management and analytics, as well as access to human intelligence in data engineering, machine learning, and business analytics. While many companies offer one or two similar products, and many consulting firms can provide guidance in choosing and implementing tools, Aunalytics integrates all the tools and expertise in one end-to-end solution built for non-technical business users. The success of a digital transformation project should not be hitting implementation milestones. The success of a digital transformation project should be measured in business outcomes.
How to Assess True Branch Profitability in Mid-Market Banking
How to Assess True Branch Profitability in Mid-Market Banking
Branch profitability calculations are critically important for branch planning. Traditionally, the branch where a customer opens an account receives credit for that customer’s business. But it’s not always that simple. Learn how analyzing the right data can lead to more accurate results.
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Aunalytics is a data platform company. We deliver insights as a service to answer your most important IT and business questions.
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Mid-Market Bank Achieves Targeted Marketing Success
Mid-Market Bank Achieves Targeted Marketing Success
Customers are increasingly demanding digital banking experiences, immediate results and responses to sales and service inquiries, and easy-to-use online platforms. While it is common for banks to invest in building mobile and online banking platforms, industry trailblazers are now harnessing the power of data and analytics to drive revenue and smarten operations.
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Aunalytics is a data platform company. We deliver insights as a service to answer your most important IT and business questions.
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The Problem with Relying on Your IT Department for Data Analytics
The Problem with Relying on Your IT Department for Data Analytics
IT departments are primarily concerned with maintaining security and keeping systems operational. IT owns the business function of minimizing internal and external security risks and vulnerabilities, and maintaining core business systems and operations. By asking your IT department to implement data analytics, you are asking them to take focus off of what they are trained to do and dabble into new areas of technology without having the expertise to do so.
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Aunalytics is a data platform company. We deliver insights as a service to answer your most important IT and business questions.
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How to Use AI for Smarter Financial Institution Service
Data has long been used in decision making in the financial services industry. Statistical scoring models based on consumer data like FICO® have been used for half a century to guide lending decisions in the financial services sector. But today’s analytics space has evolved to the point where many other factors not easily digested by credit scoring bureaus play a roll. Imagine a deeper understanding of lending risk factors not commonly reported in credit scores:
- Number of changes of residence in the past five years
- Householding status (single or cohabiting/married)
Imagine having the ability to look at a particular client and understand based on past data how that individual compares in terms of various factors driving that business relationship:
- Which of our financial products is this customer most likely to choose next?
- How likely is this customer to default or become past due on a mortgage?
- What is churn likelihood for this customer?
- What is the probable lifetime value of this customer relationship?
Aunalytics financial services experts understand the most pressing business questions specific for this industry. Working with our financial services experts, Aunalytics’ Innovation Lab data scientists have developed proprietary machine learning, AI and deep learning algorithms based on a solid understanding of the data commonly collected by financial institutions. Our data engineers understand the types of data commonly created and used by the industry, common data sources and have created integrations to bring data from across a bank together into a single analytics-ready feed. The end result is data organized into industry specific relational data marts ready to answer questions posed by business users from financial services institutions.
A SHAP value chart for a remarkably accurate loan default risk model we developed. A benchmark with testing data provided by one client was able to predict 30% of that customer’s loan defaults with 99% accuracy, or predict 75% of all loan defaults with 75% accuracy (i.e. 0.99 precision at 0.3 recall or 0.75 precision at 0.75 recall).
Take the example of a recent model we developed at Aunalytics to predict loan default risk. Looking at a chart of the SHAP (Shapley Additive Explanations) values for this model, we can see a number of common-sense observations confirmed. For example, high interest loans (represented by a pink dot in the Account_InterestRate line) and low FICO scores (represented by a blue dot in the Lend_OriginalFICOScore) positively correlate with default risk. This model discovered some much less intuitive characteristics of high risk loans as well: For some loans, payment frequency (Lend_PaymentFrequency) was actually the single most important factor for predicting loan defaults. Moreover, a well-known but not always properly appreciated factor to default risk is illustrated visually: the type of loan being underwritten (Account_ProductType) is in many cases just as important as a customer’s credit score to default risk. Auto loan applicants with high FICO scores might be more of a default risk than customers with low credit scores shopping for a home mortgage.
In so many cases, machine learning techniques enable more accurate and understandable models of risk, propensity, and customer churn because they represent a more complex model understanding of the various factors that go into risk modeling. Our models deliver greater accuracy than simpler, statistical models because they understand the relationship between multiple indicators.
Through AI and machine learning enriched data points, clients can easily understand a particular customer or product by comparing it to other customers with similar data. Whether you want to know if a customer is likely to select a new product, their default risk, churn likelihood, or any other number of questions, our data scientists and business analysts are experienced and committed to answering these questions based on years of experience with financial services businesses.
