Underwriting is basically the service-suite extended by large-scale financial institutions like banks, insurance companies, or an investment firm, wherein, they assure timely payment in the form of a financial reprieve in case the service seeker, (a company or an individual) faces substantial damage or loss. The underwriting party also embraces the financial risk for the liability which can potentially crop up from such an assurance.

The primary function of an underwriter, whether machine or human, is to collect and corroborate all relevant data regarding the loan claimant. When this lengthy process is conducted manually, it takes a long time, but now automated by the digital switch, tasks which generally took weeks to realize can now be completed in a matter of minutes.

The emergence of new-age AI-powered APIs or Application Programming Interfaces are seemingly changing the game as we speak. These services essentially enable diverse applications to interact over the internet and extract pertinent datasets from a multiplicity of sources. This ensures that the collated data is far more dependable and up-to-date, which assists in the seamless transference of the information directly into an underwriting and decisioning engine. Although not always, the API process may require the applicant to provide login credentials to a data source (such as “log in with Facebook”) or to otherwise offer overt authorisation.

In terms of practical application, what an autopilot is for an airplane, an AI decision engine can be for the lender. The hi-tech advancements in tech innovation has brought into operation superior and automated decision engines, in which the majority of the essential data is digitally siphoned into the engine. This helps in simplifying the data collection process and also reduces the processing burden, which can dramatically displace the economics of smaller loans for the lender.

A number of fintech players are actualising such a feat for small business loan products too, at the minimum for clear sanctions or refusals. However, in most scenarios, lenders catering to the needs of small businesses still desire a measure of physical supervision for sanctioning approvals. To sustain this lender preference, even the most state-of-the-art decision engines active in the market usually accord easy-to-use configuration ability on their digital platforms to enable users to select which steps should be automated, and those that necessitate manual assessment.

The author is Co-founder and CEO, Smartcoin Financials

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