Analytics in Finance
Analytics plays a central role in Finance as they provide the basis for measuring business performance and justifying funding and purchase decisions.
Why is Finance Analytics Important?
Finance is a numbers game. Asset values, revenues, profitability and cashflow projections drive valuations of businesses, which in turn govern investment and risk. Company viability can hinge on the strength of financial metrics and projections as they influence capital investment.
How Analytics are Used in Finance
Whether investing or divesting, metrics matter when making decisions. Below are some examples of financial decision-making with key analytics:
When financing a purchase, a lender uses many metrics to drive their decisions. A major consideration is past credit history which credit agencies summarize as the individual borrower’s credit score. The credit score considers factors such as the number of open accounts, settled accounts, balances and negative influences such as missed payments. Lenders need to see savings balances and cashflows to determine the risk of default before moving forward.
The finance industry uses Artificial Intelligence(AI) and Machine Learning (ML) tools to protect credit card and banking transactions from fraud by considering various factors, including historical transactions, locations and purchase types, to detect real-time anomalies.
Sales and Marketing
The financial industry analyzes customer interactions to uncover opportunities to cross-sell services for itself and partner organizations. Customers can be segmented by opportunity, and analytics such as balances and transaction volumes are used to prioritize opportunities that add value when making offers.
When considering a loan, borrowers can improve their chances by building a strong credit score by borrowing and maintaining a solid repayment history. Metrics that drive affordability include income, outstanding debt and the income-to-debt ratio.
Every bank branch needs to accurately calculate its closing balance by taking its opening cash balance (the Float), deducting dispensed cash and adding cash received. Branch performance is driven by KPIs such as the number of accounts held, customer satisfaction scores and cross-sells to existing account holders.
The Vector columnar database allowed one retail bank to reduce the time it takes to run its nightly reconciliation workload across branches from hours to 13 minutes.
The trading of currencies, commodities and stocks is very data intensive. Changes in asset values must be communicated in a split second, and trade response times and execution speeds impact profitability in a very competitive environment. Machine Learning models guide investors on oversold or undersold assets, and analyst ratings often influence trade decisions.
Refinitiv provides updates to vital metrics such as current price, PE ratios and news to its stock information subscription services in under 20 milliseconds. This is thanks partly to a server farm hosting Actian Vector columnar database instances.
Financial institutions are highly regulated and risk-averse. When the economy is strong and base interest rates are low, lenders are tempted by generous terms to get more business. When a recession hits, they can be dragged down because they have too many default loans and are considered poor quality. Institutions are often forced to write off and offload batches of low-quality loans to other lenders to balance their remaining loan portfolio quality. This practice has led to even more regulation and greater oversight of lending metrics. Banks can control risk further by limiting loan terms and applying risk-adjusted interest rates based on an analysis of borrowers’ credit histories, demographics, and individual financial circumstances.
The rise in popularity of Cryptocurrencies has been joined using smart contracts that enable peer-to-peer digital transactions without the use of an intermediary bank. One wall street startup has created a development environment to enable smart digital contracts that use the Actian Vector columnar database to gain operational insights and DataConnect to connect transaction participants by easily connecting ledgers across entities using DataConnect. Vector provides real-time analytics, including operational balances, transaction volumes and profitability.
Actian OpenROAD for Finance
The OpenROAD fourth-generation programming language lets customers quickly build portable, database-centric applications. OpenRoad provides a built-in library of Financial Analytic functions, including the following:
- The CTERM function calculates the number of compounding periods required for an investment to yield a specified future value.
- The DDB function calculates double-declining depreciation for a specified period.
- The DEPR function calculates the depreciation of assets.
- The IPMT function calculates the interest portion of a payment for a specified period.
- The NPER function determines the number of periods required for an investment to mature or a loan to be repaid.
- The PMT function calculates the periodic payment for a loan, given the principal, interest rate, and number of periods in the life of the loan.
The Actian Data Platform
Leading Financial Services and Banking customers rely on the Actian Data Platform to analyze risk positions, identify fraud, meet compliance requirements, reduce data onboarding time, improve time to value, and enhance revenue recognition. The Actian Data Platform provides a multi-cloud solution to run analytics on-prem and in the cloud.