A Forrester Report suggests that by 2030, banking would be invisible, connected, insights-driven, and purposeful. ‘Trust’ will be key for building the industry in the future.
But how do banks and FinTechs enable an excellent customer experience (CX) that translates into “trust” when the onboarding experience itself is time-consuming and prone to error. The disengagement is clear from industry reports. 85% of corporates complained that the KYC experience was poor. Worse, 12% of corporate customers changed banks due to the “poor” customer experience.
Losing a customer is disastrous because the investment and effort that goes into the process are immense. Both KYC and Customer Lifecycle Management (CLM) are expensive and time-consuming. Banks could employ hundreds of staff for a high-risk client for procuring, analyzing, and validating documents. Thomson Reuters reports that, on average, banks use 307 employees for KYC. They spend $40 million (on average) to onboard new clients. When a customer defects due to poor customer engagement, it is a double whammy for the bank. It loses a client and has to work harder to cover the costs of the investment made. Industry reports indicate that new customer acquisition is five times costly than retaining an existing one.
The same scenario is applicable for financial companies, which must be very careful about who they take in as clients. As a result, FinTechs struggle with greater demand for customer-centricity while fending competition from challengers. By investing in digital transformation initiatives like digital KYC, many challenger banks and FinTechs deliver exceptional CX outcomes and gain a foothold.
Today Commercial Banks and FinTechs cannot afford to overlook regulatory measures, anti-terrorism, anti-money laundering (AML) standards, and legislation, violations of which would incur hefty fines and lead to reputational damage. The essence of KYC is to create a robust, transparent, and up-to-date profile of the customer. Banks and FinTechs investigate the source of their wealth, ownership of accounts, and how they manage their assets. Scandals like Wirecard have a domino effect, and so banks must flag off inconsistencies in real-time. As a result, banks and FinTechs have teamed up with digital transformation partners and are using emerging technologies AI, ML, and NLP to make their operations frictionless and customer-centric.
Decoding existing paint-points and examining the need for a comprehensive data extraction tool to facilitate seamless KYC
Long time-to-revenue results in poor CX
Customer disengagement in the financial sector is common. Every year, financial companies lose revenue due to poor CX. Here the prime culprit for customer dissatisfaction is the prolonged time-to-revenue. High-risk clients average 90-120 days for KYC and onboarding.
The two pain points are – poor data management and traditional methods for extracting data from documents (predominantly manual). Banking c-suite executives concede that poor data management arising due to silos and centralized architecture is responsible for high time-to-revenue.
The rise of exhaust data
Traditionally, KYC involved checks on data sources such as ownership documents, stakeholder documents, and the social security/ identity checks of every corporate employee. But today, the KYC/investigation is incomplete without verification of exhaust data. And in the evolving business landscape, it is exigent that FinTech and banks take exhaust data into account.
Emerging technologies like AI, ML, and NLP make onboarding and Client Lifecycle Management (CLM) transparent and robust. With an end-to-end CLM solution, banks and FinTech can benefit from an API-first ecosystem that supports a managed-by-exception approach. An API-first ecosystem that supports an exception management approach is ideal for medium to low-risk clients. Data management tools that can extract data from complex documents and read like humans elevate the CX and save banks precious time and money.
Sheer volume of paperwork prolongs onboarding.
The amount of paperwork accompanying the onboarding and KYC process is humongous. When it comes to business or institutional accounts, banks must verify every person’s existence on the payroll. Apart from social security and identity checks, ultimate beneficial owners (UBO), and politically exposed persons (PEP), banks would have to cross-examine documents related to the organization’s structure. Verifying the ownership of the organization and the beneficiaries’ check adds to the complexity. After that, corroborating data with media checks and undertaking corporate analysis to develop a risk profile. With this kind of paperwork involved, KYC could take days.
However, as this is a low-complexity task, it is profitable to invest in AI. Instead of employing teams to extract and verify data, banks and FinTechs can use data extraction and comprehension tools (powered with AI and enabled with machine learning) to accelerate paperwork processes. These tools digitize documents and extract data from structured and unstructured documents, and as the tool evolves with time, it detects and learns from document patterns. ML and NLP have that advantage over legacy systems – learning from iterations.
Walking the tightrope (between compliance and quick TOI)
Over the years, the kind of regulatory framework that America has adopted to mitigate financial crimes has become highly complex. There are multiple checks at multiple levels, and enterprise-wide compliance is desired. Running a KYC engages both back and front office operations. With changing regulations, Banks and FinTechs must ensure that KYC policies and processes are up-to-date. Ensuring that customers meet their KYC obligations across jurisdictions is time-consuming and prolonged if done manually. Hence, an AI-enabled tool is needed to speed up processes and provide a 360-degree view and assess the risk exposure.
In 2001, the Patriot Act came into existence to counter terrorist and money laundering activities. KYC became mandatory. In 2018, the U.S. Financial Crimes Enforcement Network (FinCEN) incorporated a new requirement for banks. They had to verify the “identity of natural persons of legal entity customers who own, control, and profit from companies when those organizations open accounts.” Hefty fines are levied if banks fail to execute due diligence as mandated.
If they are to rely on manual efforts alone, banks and FinTechs will find it challenging to ensure CX and quick time-to-revenue while adhering to regulations. To accelerate the pace of operations, they need tools that can parse through data with greater accuracy and reliance than the human brain. And also can learn from processes.
No time for perpetual KYC as banks struggle with basic KYC
For most low and medium-risk customers, a straight-through-processing (STF) of data would be ideal. It reduces errors and time to revenue. Client Lifecycle Management is essential in today’s business environment as it involves ensuring customers are compliant through all stages and events in their lifecycle with their financial institution. That would include raking through exhaust data and traditional data from time to time to identify gaps.
A powerful document extraction and comprehension tool is therefore no longer an option but a prime requirement.
Document extraction and comprehension tool: how it works
Document digitization: IDP begins with document digitization. Documents that are not in digital format are scanned.
OCR: Next step is to read the text. OCR does the job. Many organizations use multiple OCRS for accuracy.
NLP: Recognition of text follows the reading of the text. With NLP, words, sentences, and paragraphs are provided a meaning. NLP uses sentiment analysis, part of speech tagging, and making it easier to draw a relation.
Classification of documents: Manual categorization of documents is another lengthy process that is tackled by IDP’s classification engine. Here machine learning (ML) tools are employed to recognize the kinds of documents and feed them to the system.
Extraction: The penultimate step in IDP is data extraction. It consists of labeling all expected information within a document and extracting specific data elements like dates, names, numbers, etc.
Data Validation: Once the data has been extracted, it is combined and pre-defined validation rules based on AI check for accuracy and flag off errors, improving the quality of extracted data.
Integration/Release: Once the data has been validated/checked, the documents and images are exported to business processes or workflows.
The future is automation!
The future is automation. An enriched customer experience begins with automation. To win customer trust, commercial banks and FinTechs must ensure regulation compliance, improve CX, reduce the costs by incorporating AI and ML and ensure a swifter onboarding process. In the future, banks and FinTechs that improvise their digital transformation initiatives and enable faster and smoother onboarding and customer lifecycle management will facilitate deeper customer engagement. They would have gained an edge. Others would struggle in an unrelenting business landscape.
True, there is no single standard for KYC in the banking and FinTech industry. The industry is as vast as the number of players. There are challengers/start-ups and decades-old financial institutions that coexist. However, there is no question that data-driven KYC powered by AI, ML brings greater efficiency and drives customer satisfaction.
A tool like Magic DeepSight™ is a one-stop solution for comprehensive data extraction, transformation, and delivery from a wide range of unstructured data sources. Going beyond data extraction, Magic DeepSight™ leverages AI, ML, and NLP technologies to drive exceptional results for banks and FinTechs. It is a complete solution as it integrates with other technologies such as API, RPA, smart contract, etc., to ensure frictionless KYC and onboarding. That is what the millennial banks and FinTechs need.