Modern business intelligence relies heavily on the application and statistical analysis of raw data. Most of this is garnered through thorough data mining. Due primarily to consumers' use of comparative online shopping for all sorts of goods and services, solid predictive models and business decisions can be made using those available data analytics. This grants service providers a strong competitive edge when shaping marketing strategies aimed at specific demographics.

With that in mind, financial lenders can benefit from the implementation of data analysis when researching a potential borrower's background and risk factors. From establishing monthly payments to interest rates, today's lenders can seamlessly apply exploratory data analysis to various data sets, ultimately gaining valuable insights in creating statistical models. Here, we will look at four key ways that lenders can use data analysis software for accurate assessments and outreach.

1. Predictive Analytics and Repayments

Regardless of the type of loan being considered, one of the most important aspects of a lender's ultimate decision is the assessing of weekly, fortnightly or monthly repayments. This core factor can affect repayment frequency, as well as the amount of interest attached to the loan repayments themselves. A lender can apply data analysis from various data sets in order to weigh the pros and cons of a loan, specifically through statistical models of repayment templates. Information that can provide valuable insights may include existing loan types, the backgrounds and financial situations of different demographics, and the risk factors involved in either offering higher or lower interest rates. Coupling this quantitative data with a mortgage calculator can make a fairly accurate prediction regarding a client's potential.

Although a data scientist can assist in creating these models, data visualization software platforms can also digest different types of data from the same sources, ultimately presenting a workable set of stats with data analysis tools. If a lender recently initiated a relationship with a new borrower, these tools can be beneficial in creating repayment options based on average credit situations and previous loan applications. Here, data science can effectively determine a workable hypothesis for repayment amount and other additional payments.

2. Risk Management and New Borrowers

For financial lenders, the most important decision making aspects include a potential client's ability to meet their monthly repayment installments and, ultimately, the loan balance. So, what is a data analysis process going to provide the lender as far as valuable insight? Well, that depends on the type of loan, as well as the type of data analysis is being conducted. For example, if the client is a homeowner looking to renegotiate their mortgage payment, their credit score, and repayment history both carry a lot of weight in the decision. Likewise, predictive analysis using that client's current financial standing as a viable data source can gain valid, actionable insights about their future needs.

3. Analytics and Long-Term Clients

For lenders working with established, long-term clients, the data integrity speaks for itself. However, if a homeowner is seeking a home loan for new needs, such as home improvements or a remodel project, their repayment history is the most useful information already at the lender's disposal. That historical data can be useful in determining how much money the client can be granted for their loan amount, as well as the terms of both the repayment amount and the amount of interest. An assessment of that client's previous payment history is also a good idea for predictive analytics implementation.

4. Customer Outreach and Self-Assessment

A financial lender can also use different types of data for the overall success of their business in other areas. For example, search engine optimization (SEO) and online presence can be useful information in self-assessment and customer outreach. Here, the data analysis process includes a wide range of big data, including customer feedback and purchase history, plus preferences as stated on social media profiles and internet searches. Also known as sentiment analysis, these algorithms can be translated into readable visual formats for team statisticians and marketing firms to strategize further outreach.