20 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You’ll start by scanning Outlook for urgent emails from loan officers or clients, then open Excel and SQL to update credit models and pull recent payment histories. Expect several meetings: a 30–60 minute call with relationship managers to review new credit applications and a short status check with collections on delinquent accounts.
Afternoons often mean running ratio reports in SAS or Python, updating customer files in SAP or Word, and drafting a one‑page credit memo or PowerPoint for credit committee review. End the day monitoring risk trends and jotting any compliance issues to follow up tomorrow.
Start with Excel — pivot tables, VLOOKUP/XLOOKUP, and basic VBA macros are used daily to generate financial ratios and compare industry/geographic data. Most lenders expect clean Excel models for credit analysis.
Next learn SQL (Microsoft SQL Server/Structured query language) to pull transaction and payment data. Then pick up basics of SAS or Python for statistical analyses and automation. Familiarity with SAP, Word, Outlook, and PowerPoint helps with files, communication, and committee decks.
Use Python for reproducible tasks: cleaning transaction data, calculating ratios, or running simple predictive models, but not as a final decision-maker. Always validate model outputs against known cases and document assumptions so a human can audit the logic.
Don’t feed private customer documents into public AI services. Keep models and scripts in your bank’s secure environment (SQL Server, internal Git, or SAS) and involve compliance when automating credit limits or collection prioritization.
Salaries vary by country and bank size. Entry banking analysts at regional banks often start near the market median for analysts of their city; in many U.S. cities that’s roughly $55,000–$75,000 annually. Large investment banks or major financial centers typically pay more.
Also expect bonuses tied to accuracy and team performance. Ask recruiters for the bank’s pay bands and whether pay includes base, bonus, and benefits like pension or health coverage.
Begin by studying financial statements and basic credit ratios: current ratio, debt/EBITDA, interest coverage. Use Excel to calculate these from sample balance sheets and income statements; many online courses provide templates.
Then practice by reviewing mock credit applications and writing short memos recommending credit limits. Learn the bank’s regulatory rules and typical documentation kept in customer files (financial statements, repayment schedules). Volunteering or internships in operations or collections gives quick, practical exposure.
They overlap a lot: 'credit analyst' focuses specifically on assessing borrowers’ ability to repay, analysing credit history, and advising on credit ratings. A 'banking analyst' can be broader, covering sales support, monitoring risk trends, and internal reporting as well as credit work.
In many banks the roles are identical and use the same systems (Excel, SQL, SAS). If the posting mentions collections, setting customer credit limits, and maintaining customer files, it’s mostly credit work. If it adds project finance or product pricing, the role is broader.
Excel modeling: show a clean spreadsheet where you calculate key ratios from a sample financial statement and explain what each ratio signals about repayment ability. Bring printed examples or a screen share.
Data querying: demonstrate basic SQL queries you’ve written to pull transactions or balances. Even simple SELECT and JOIN examples show you can get raw data for analysis. Communication: prepare a one‑page credit memo or short slide that states recommendation, supporting ratios, and main risks — that shows you can confer with clients and advise on credit rating.