Product Propensity Report (2)

What is a Product Propensity Report?

Cultivating a long-term customer relationship requires intelligent marketing efforts based on a solid understanding of the types of products a particular customer or group of customers would most likely be interested in based on their past behavior. Aunalytics has nearly a decade’s worth of experience assisting our clients with data analysis projects and now offers the fruits of that experience as the Product Propensity Report Framework for our Daybreak™ Analytic Database subscribers. The Product Propensity Report Framework (PPRF) provides a customized dataset with an individualized analysis of every customer’s propensity for new financial services products by type. Think of it like a PivotTable in Excel with a row for every customer and columns representing each of the products your institution offers with a propensity score showing the likelihood that customer might opt for that product.

Product Propensity Report table

The preceding example is purely illustrative since every Product Propensity Report is customized to the types of products and services offered by our clients, but it shows the general structure of how such a report might look like for a client offering five different products. In this example, customers receive a decimal score (0-1) for each product, or an indication that the customer already has that product (NA). In this example, we can see that Jane Smith is predicted to be very likely to open a checking account or possibly a credit card account, whereas John Doe and Jill Ramos will likely open a credit card account, and Jack Jones will most likely be interested in a retirement account.

Aunalytics generates these Product Propensity Reports for clients based on a machine learning algorithm that learns about new accounts opened from your organization’s historical data. This algorithm digs into data about transactions and current product utilization to construct a model that can identify future customers’ propensities based on patterns in this data. The output of this model is tailored uniquely to your organization’s product offerings and trained on your organization’s data in order to provide you with reporting that gives you a deeper glimpse into what your customers are after and how best to tailor your outreach to connect those customers with the products and services they are most interested in.

Daybreak Insights

Visualize Data-Driven Answers with Daybreak Insights

Daybreak Insights ScreenshotDaybreak™ Insights provide best-in-industry value for analytics platforms by integrating data visualization to display results of AI and Machine Learning powered analytics assessment of your data. Daybreak exists to enable anyone to get data-driven answers to their questions. With easy-to-use tools like Daybreak Natural Language Answers, users without a background in database technology can generate result sets from their data by simply asking questions about the data. No coding, or technical knowledge of databases, datamarts, SQL queries or how to get your data prepped and ready for analysis is needed. Insights are revealed based upon your data in the form of lists of customer records that match your query and also by visualizations to display those results in various graphical forms like line, donut, or bar charts. This tool provides similar capabilities to dedicated BI tools like Tableau or PowerBI, and is available as part of Daybreak. Daybreak also allows you to export analytic results to your favorite BI visualization tool, where your team may have already built custom views and dashboards.

Insights are a valuable way to share stories about data with other members of your organization. The Daybreak Insights dashboard enables you to build a custom dashboard for saved queries, and then share that dashboard with other Daybreak users on your team. Insights can also be saved as downloadable graphics for inclusion in other documents or slideshows to accelerate and automate data-driven presentations.

Daybreak Insight Donut View

Figure 1: Daybreak Insight showing the proportion of business vs. consumer customers

Daybreak Insight Line View

Figure 2: Daybreak Insight showing the number of customers joining over time

Daybreak Insights are easy to create. Simply click the “Insights” tab to add an Insight. Daybreak supports different kinds of charts to display different kinds of data, like timeseries line charts, column or donut charts, or summary metrics to display simple calculations based on fields. Each type of Insight has its own settings that can be configured to control how the charts are drawn. The value of each axis or segment can be simple counts of records, or aggregations based on arithmetical or statistical operations like sums, minimums, maximums, or averages.

Insights are one of the many ways Aunalytics provides data-driven answers to your most important business and IT questions.

Loan agreement with fountain pen

What is the Aunalytics Loan Default Risk Smart Feature?

Loan agreement document with fountain pen

Loan underwriting represents one of the most significant risk factors for financial institutions. Through nearly a decade of work with financial institutions, Aunalytics has developed an algorithm for training machine learning models that learn from your organization’s historical loan default data about the most significant factors predicting loan defaults among your customers. The Aunalytics Loan Default Risk Smart Feature™ provides a score of loan default risk based only on data available at the time of a credit application. 

Our multi-indicator risk model significantly improves upon the accuracy provided by purely statistically-based risk models such as FICO scores and the actuarial tables used by underwriters to set interest rates. Although these basic statistical models are general guides to default risk, our model calibrates and quantifies how those variables and others impact loan defaults across loans of all types. For example, although low FICO scores are predictive of higher risk, how predictive they are depends on other factors such as the type of loan (mortgages, HELOCs, business loans, etc.), term, payment frequency, and so forth. For example, in one model trained using our proprietary algorithm on loan default data from one of our clients, the model discovered that for small business loans, payment frequency was actually more predictive of loan default than credit score. In other words, even borrowers with high credit scores would default on small business loans with monthly payment terms at a rate that wasn’t that different from borrowers with low scores. When payments were made semi-annually, however, credit scores became a much more predictive factor of loan default risk. 

By digging deep into the complex relationships between different loan risk factors, our algorithm can produce impressive results. Performance tests on datasets have shown this algorithm to be highly precise, with one model trained on one of our client’s data the ability to predict 30% of loan defaults in a benchmark dataset with near perfect accuracy (0.99 precision at 0.30 recall) and could return consistently reliable results at higher levels of recall. The loan default risk models trained with our proprietary algorithm provide extraordinary business value to our clients in the financial services industry by flagging risky loans and providing a deeper understanding of risk factors involved with loan applications beyond basic ratings provided by an applicant’s FICO score. 

Aunalytics is a data platform company delivering answers for your business. Aunalytics provides Insights-as-a-Service to answer enterprise and mid-sized companies’ most important IT and business questions. The Aunalytics® cloud-native data platform is built for universal data access, advanced analytics and AI while unifying disparate data silos into a single golden record of accurate, actionable business information. Its DaybreakTM industry intelligent data mart combined with the power of the Aunalytics data platform provides industry-specific data models with built-in queries and AI to ensure access to timely, accurate data and answers to critical business and IT questions. Through its side-by-side digital transformation model, Aunalytics provides on-demand scalable access to technology, data science, and AI experts to seamlessly transform customers’ businesses.

Ransomware, cloud attacks, phishing icons

Top 10 Cyberattacked Industries in 2020

Share of cyberattacks by industry
Source: IBM Security XForce

According to IBM in its X-Force Threat Intelligence Index, the top 10 industries suffering the most cyberattacks in 2020 were:

1. Finance and Insurance

Most Common Type of Attack: Server access attack

Since 2016, the finance and insurance industry has been the most attacked industry. In 2020, attacks increased over 2019 by 238%. Hackers seeking to profit financially from attacks often hit this industry. Paralyzing banks is usually less of a goal with attacks on this industry, but accessing internal systems can yield hefty illicit returns. However, during the pandemic, hackers seeking to paralyze infrastructure including nation-state cyber criminals also hit banking institutions to cause chaos.

2. Manufacturing

Most Common Type of Attack: Ransomware

The attacks against manufacturers doubled in 2020 compared to 2019. 21% of all 2020 ransomware cyberattacks hit the manufacturing industry. However, this industry also saw 4X more BEC attacks than any other industry, and a significant number of data theft attacks. Hackers renewed interest in this industry, likely trying to take advantage of supply chain disruption and operational chaos caused by the global pandemic, as consumers saw (and continue to experience) shortages in manufactured goods.

3. Energy

Most Common Type of Attack: Data theft

35% of attacks in the energy sector involved data theft, while only 6% involved ransomware. This was likely indicative of hacker motivations to hit this industry including IP theft, customer data theft and extorsion. BEC and server access attacks were also notable in this industry.

4. Retail

Most Common Type of Attack: Credential theft

Attacks on the retail industry were actually lower in 2020 than in 2019. This was likely due to fewer retail transactions taking place during the 2020 (less room to hide as a hacker). Retail is typically a target because of the high volume of credit card and financial transactions.

5. Professional Services

Most Common Type of Attack: Ransomware

Professional services saw the highest percentage of attacks from ransomware attacks of any industry. Data theft and server access attacks were also common in 2020. These organizations are typically attractive to cyber criminals because they serve as a path to further victims and often hold confidential data about customers.

6. Government

Most Common Type of Attack: Ransomware

Government received the second highest number of ransomware attacks of the industries, totaling a third of the attacks that this industry faced. Yet, only 38% of state and local government employees have been trained on ransomware prevention. This industry also faced a large burden in moving operations to accommodate work from home environments, as much of this industry had all team members working on site, and was not equipped for moving a remote workforce. Data theft attacks were also notable.

7. Healthcare

Most Common Type of Attack: Ransomware

The healthcare industry suffered twice as many cyberattacks in 2020 than in 2019, likely due to hackers taking advantage of operational chaos caused by the global pandemic, shifts in workforce to cover emergency medical care while furloughing operational and administrative staff due to revenue challenges caused by elective medical services being put on hold. Hackers, including nation-states looking to disrupt and steal data from organizations in this industry, targeted those in medical research and development attempting to invent COVID-19 vaccines, as well as frontline providers.

8. Media

Most Common Type of Attack: Malicious domain name squatting

90% of malicious domain name spoofing attempts targeted the media. This sector includes telecommunications and mobile communications providers, as well as media and social media outlets that can play a critical role in political outcomes, especially during election years. The timing of 2020 being a U.S. presidential election year likely drove this type of attack in this industry.

9. Transportation

Most Common Type of Attack: Malicious insider / misconfiguration

Attacks against the transportation industry were much lower in 2020, likely because everyone was sheltering in place and travel bans existed across the globe during 2020. This industry ranked #3 in 2019 and fell to #9 in 2020.

10. Education

Most Common Type of Attack: Spam / adware

Education has historically been vulnerable to cyberattacks due to a large decentralized surface area of users hard to control with staff and students regularly logging into systems from home to complete and grade course work. However, the user and device based security measures that this distributed surface area drove for security protection before the pandemic, better equipped education for security operations during the pandemic than other industries accustomed to relying on firewall protection.

BioCrossroads Listing

Aunalytics has been included in the 2021 BioCrossroads Book of Data and Organizations

Aunalytics has been included in the 2021 BioCrossroads Book of Data and Organizations directory of Indiana enterprises in the health-data intersection – it includes information on Indiana’s industry, government, health systems, academia, and digital health startups that have tremendous data and technology resources which are driving transformative healthcare and life sciences work locally and globally. The directory now includes snapshots of 40 organizations who control data assets – data sets, data talent, and/or data technology — as well as five cross-organizational initiatives, who collaborating for a specific data project or program.

Aunalytics, Inc. Profile


  • Aunalytics is a data platform company delivering answers for your business. Aunalytics provides Insights-as-a-Service to answer enterprise and midsized companies’ most important IT and business questions. The Aunalytics® cloud-native data platform is built for universal data access, advanced analytics and AI while unifying disparate data silos into a single golden record of accurate, actionable business information. Its DaybreakTM industry intelligent data mart combined with the power of the Aunalytics data platform provides industry-specific data models with built-in queries and AI to ensure access to timely, accurate data and answers to critical business and IT questions. Through its side-by-side digital transformation model, Aunalytics provides on-demand scalable access to technology, data science, and AI experts to seamlessly transform customers businesses.
  • Our healthcare industry revenue cycle management analytics focus provides AI and machine learning driven insights for reducing insurance reimbursement underpayments and streamlining billing to provide a data driven practice management solution.


We use data and technology to improve the lives of others.


Aunalytics began in 2011 when successful data center entrepreneurs in South Bend, Indiana partnered with data scientists from the University of Notre Dame to provide end to end business and IT analytic solutions backed by a cloud data center. Aunalytics aims to bring data and analytics to mid-market businesses, healthcare systems, and providers by our side by side model to provide clients with access to our data engineer and data science experts to harness data driven insights to better compete with large nationals. We provide healthcare industry specific data models and mine your data for insights to reduce operational costs and drive revenue.


  • 250 team members in Indiana, Michigan and Ohio
  • Enterprise cloud, data analytics, data management and managed services offerings powered by our data platform
  • Industry specific data analytics – built for healthcare providers for revenue cycle management
  • HIPAA compliant enterprise cloud

Churn Algorithm

What is the Aunalytics ‘Propensity to Churn’ Smart Feature?

Customer churn represents lost opportunities as customers move their business to one of your competitors. At Aunalytics, we have nearly a decade of experience developing such models for clients in various industries, including financial institutions. For client subscribers to our Daybreak™ Analytical Database, the churn propensity report Smart FeatureTM offers a uniquely valuable dataset for understanding the risk factors that could indicate customers are about to take their business elsewhere.

The algorithm behind this Smart Feature was developed by studying historical customer churn data from many different client institutions to discover common patterns in the typical behaviors that precede account churn. One key approach to developing this algorithm was to redefine churn beyond simply customers who closed an account; instead, churn was defined to include customers who suddenly drew down a high-balance account and never returned that account balance to its previous level after a period of months. By looking at the data in this way, our data scientists were able to understand that the behaviors that predict churn often happen many months or years before an account is actually closed—for example, a customer stops using direct deposit or bill pay services, then withdraws most of the balance a few weeks to months later but doesn’t actually close the account for a few years.

Churn AlgorithmBased on this deeper look at the data, Aunalytics has been able to develop an algorithm that looks at numerous factors such as customer transactional data, the age of the account, the presence of ACH deposits prior to churn, number of debit card transactions per month, online banking login frequency, and length of the total customer relationship, among others. Once this algorithm is trained on a client’s data, we can provide that client with propensity to churn scores expressed both as a standard percentile (e.g. absolute probability in the range of 0-100%) and a decile score (e.g. a score from 1-10 where 1 is the group composed of the 10% of customers least likely to churn and 10 is the 10% most likely to churn). Using these Smart Features, our clients in financial services can understand immediately what the likelihood is that a customer will churn, or build datasets to target customers at risk of churn with offers and incentives that might keep them actively using those financial products.

How to Identify Which of Your Banking Customers are at Risk for Crypto Scams

The allure of investing early in the “next big thing” has led to interest in investing in cryptocurrency. Cryptocurrency is a type of digital or virtual money that exists in electronic form (no printed dollars or coins). As a new industry, it is highly unregulated compared to other types of investments and banking. While there is potential for a big win, there is strong potential for a big loss.

Crypto Buyer Profile

Crypto investors tend to be younger adults. They decide to put money into a crypto currency investment with the idea that the currency will gain value over time. When compared to current low and no interest checking and savings accounts rates, where money will not grow in a conventional banking account, crypto provides the promise of potential growth.

People use cryptocurrency for quick payments, to avoid transaction fees that regular banks charge, or because it offers some anonymity. It is typically exchanged person to person online (such as over a phone or computer) without an intermediary like a bank. This means that there is often no one to turn to if there is a problem with the exchange.

Crypto Risks

While some crypto investment opportunities may be speculative long term plays, unfortunately unscrupulous scammers have been attracted to the emerging industry. Investors may find their investment to be worth nothing and the value stolen by a scammer. New crypto currency brands emerge often and many consumers do not know which are legitimate. Because crypto currency is stored in a digital wallet, it is at risk from hackers stealing it. Because there is no banking intermediary, there is no one to turn to if there is fraud.

Crypto currencies are not backed by the government. So, if you store your crypto with a third party company that disappears or is hacked and your money is stolen, there is little to no recourse and the government has no obligation to help you get your money back.

Unlike with credit card purchases, returns and refunds are often not possible for purchases made with crypto because the unregulated industry does not offer these protections as standard.

Crypto currency values change rapidly and constantly. Value is based upon supply and demand. Unlike many investments that may fall in value but typically regain value over time, crypto is less stable. A purchase of $1000 could fall to a value of $100 in minutes. Conversely, it could rise to a value of $10,000 in minutes. But then it could change again – even while you are trying to cash out on your gain while up, it could result in a loss due to the value changing again before the transaction is complete.

How can you protect your banking customers from crypto fraud?

DaybreakTM for Financial Institutions was built specifically for mid-market banks and credit unions to get business outcomes using analytics. Daybreak can mine your transactional data to determine which of your customers has money leaving or entering your bank from a crypto currency company. This will give you an actionable list of customers for outreach and education on risks. If enriched with a listing of crypto companies known to be fraudulent, targeted outreach may be done by you to save your customers from fraud. Further, after explaining the risks of crypto and educating your customers, you may be able to offer an alternative investment opportunity to keep your customers safe and their dollars in your bank.

Natural Language Answers screenshot

What are Natural Language Answers?

Imagine the ability to get answers from the data in your organization by simply asking a question like “how many residential accounts opened last month,” “give me a list of all accounts 90-days overdue,” or “products with monthly sales volume over 1,000 units.” Over the past few decades, advances in machine learning have made it easy to get answers from search engines like Google using natural language questions. Consumers can easily search for products and services by entering phrases like “Find mortgage lenders near me” or “grocery stores within five miles.”

Natural Language Answers UI

At Aunalytics, we understand that the competitive edge in today’s business environment depends on data-driven decisions based on a dependable and accurate datamart. To that end, we have built the Daybreak™ analytical database to provide a relational data model of industry focused data points that deliver maximum value to businesses in a specific industry like financial services, healthcare billing, or retail.

Daybreak Natural Language Answers™ extends the power of this data to all members your business. Natural Language Answers is a machine learning solution that turns finding answers to industry specific business questions into a Google-like experience for your datamart. Finding answers is easy using natural language search phrases. Just ask a question and our machine learning models will translate your question into a structured query and return results that you can download as CSV files, or visualize as bar charts, line charts, or other graphs in the Insights dashboard within the Daybreak app.

Natural Language Answers brings the revolutionary power of search that consumers have had access to since the birth of the modern search engine to business users looking for answers within their proprietary data. Business leaders with an eye to the future will understand just how revolutionary a data search tool like Natural Language Answers can be to operations. Just as it is difficult to remember life before the advent of the search engine, the advent of data search tools like Natural Language Answers represents a similar transformation in decision making for business in the next ten years.

Inputs for data lake

Why the Data Lake – Benefits and Drawbacks

A data lake solves the problem of having disparate data sources living in different applications, databases and other data silos. While traditional data warehouses brought data together into one place, they typically took quite a bit of time to build due to the complex data management operations required to transform the data as it was transferred into the on-premise infrastructure of the warehouse. This led to the development of the data lake – a quick and easy cloud-based solution for bringing data together into one place. Data lake popularity has climbed significantly since launch, as APIs quickly connect data sources to the data lake to bring data together. Data lakes have redefined ETL (extract, transform and load) as ELT, as data is quickly loaded and transforming it is left for later.

However, data in data lakes is not organized, connected, and made usable as a single source of truth. The problem with disparate data sources has only been moved to a different portion of the process. Data lakes do not automatically combine data from the multiple relocated sources together for analytics, reporting, and other uses. Data lakes lack data management, such as master data management, data quality, governance, and data accuracy technologies that produce trusted data available for use across an organization.

Solutions, such as the cloud-native AunsightTM Golden Record, bring data accuracy, matching, and merging to the lake. In this manner, data lakes can have the data management of data warehouses yet remain nimble as cloud solutions. Ultimately, the goal is to bring the data from the multiple data silos together for better analytics, accurate executive reporting, and customer 360 and product 360 views for better decision-making. This requires a data management solution that normalizes data of different forms and formats, to bring it into a single data model ready for dashboards, analytics, and queries. Pairing a data lake with a cloud-native data management solution with built in governance provides faster data integration success and analytics-ready data than traditional data warehouse technologies.

Aunsight Golden Record takes data lakes a step further by not only aggregating disparate data, but also cleansing data to reduce errors and matching and merging it together into a single source of accurate business information – giving you access to consistent trusted data across your organization in real-time.  

External Relationships Smart Feature

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.

SHAP graph for loan default risk model

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.

Learn more about Smart Features