Financial Services

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.

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.

Daybreak^TM 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.

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.

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.