Analytics techniques can also play a significant role in the early warning, detection and monitoring of fraud. These techniques allow organisations to extract, analyse, interpret and transform business data to help detect potential instances of fraud and implement effective fraud monitoring programmes.
Advanced data science techniques could enable institutions to improve underwriting decisions and increase revenues while reducing risk costs. These techniques can be fruitful across all asset classes, all types of credit risk models and the entire credit life cycle, including profit maximization and portfolio management.
For debt collections and recoveries, analytics is a critical part of the process, as it can enable organisations to create an accurate picture of the customers' propensity and ability to pay and, therefore, the amount likely to be recovered. This behavioral scoring is used to segment customers and priorities' collections activities to maximize recoveries and reduce collections costs.
Today's knowledge economy provides businesses of all kinds with access to big data that's growing exponentially in volume, variety, velocity and complexity. With more data coming from more sources faster than ever, the questions will only continue to unfold. Some examples:
- What is your organisation's data science strategy?
- How is your enterprise combining new and existing data sources to make better decisions?
- How could new data sources, including social, sensors, location and video, help improve your organisation's business performance?
- Will your organisation take advantage of big data or remain paralysed through endless analysis?
A savvy, experienced team of data science consultants can help create a roadmap that results in a meaningful, business-aligned approach to data science. The best approach is to start small rather than setting off a big bang. The mantra for successful data science projects depends on the organization's business objectives, but one constant is focus and agility.
Experts can develop an initial proof of concept by analyzing the internal, external, structured and unstructured data and conclude with meaningful, business-aligned recommendations. Data science offers endless growth opportunities to financial services and banks need to scrutinize their data for invaluable insights before it is too late.
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