20 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You spend most of your day reading financial statements, running ratios in Excel, and updating customer files in the loan system (often SAP or Microsoft SQL Server). Mornings often start with checking Outlook for client requests and new credit applications to review.
Afternoons are for deeper tasks: calling clients for missing documents, pulling credit bureau reports, running SAS or SQL queries for portfolio risk trends, and writing a recommendation or PowerPoint for a credit committee.
Start with Microsoft Excel: build common financial ratios (debt service coverage, current ratio, interest coverage) and learn pivot tables. Excel is used daily to analyze statements and set credit limits.
Next, learn SQL basics to pull data from Microsoft SQL Server and practice SAS if the employer uses it for risk modeling. Familiarity with Outlook and Word for reports and PowerPoint for committee presentations is also expected.
Use AI to speed paperwork: summarize long financial statements, draft client emails, or generate initial ratio calculations. But always verify numbers against source documents and your Excel models; AI can hallucinate figures or misinterpret regulations.
Never use AI to make final credit decisions or to produce compliance-sensitive outputs without human review. Keep audit trails: save original reports from SQL/SAP and your validated Excel models.
A credit analyst focuses on analysing financial statements, ratios, credit history, and industry data to assess risk—using Excel, SQL, SAS, and credit bureau reports. You prepare recommendations and set credit limits.
A loan officer sells loans and collects applications; an underwriter makes the final approval in many firms. In smaller shops one person may do all three jobs, but the analyst role is heavier on quantitative analysis and trend monitoring.
Learn accounting basics (income statement, balance sheet, cash flow) and three to five financial ratios. Practice building simple credit models in Excel and learn how to pull and clean data with basic SQL.
Get comfortable reading an industry report and comparing geographic or sector data. Employers expect accuracy, attention to detail, and clear writing in Word or PowerPoint for recommendations.
You will generate and monitor financial ratios (DSCR, leverage ratios, current ratio) and track delinquency rates and aging reports for collections. Use Excel and SAS to produce trend reports for management and sales.
Expect to run portfolio risk queries in Microsoft SQL Server, compare industry/geographic data, and update management with PowerPoint slides showing exposures and projected sales impact.
Accuracy with numbers, curiosity to chase missing data, and the discipline to document sources in customer files matter most—regulators and auditors will check your work. You must reconcile figures between statements, Excel models, and systems like SAP or SQL.
Good phone and email skills are important for collecting documents and resolving credit issues. Also, being able to explain your recommendation clearly in a short Word memo or a one-slide PowerPoint helps credit committees decide.