The Fintech Revolution: How To Mitigate Fraud And Manage Risk In The Digital Lending Space
The era of digital transformation is upon us, and the fintech industry stands at the helm of a revolution. The days of customers visiting physical branches of banks or NBFCs for each singular operational and transactional needs are long over, with almost every bank and NBFC providing online services via multi-channels such as web, mobile, POS machines, ATMs, smartwatches, etc.
Having said this, however, we need to take into account the fact that owing to the increase in digital adoption, there has also been a proportional increase in financial frauds. Taking into consideration the RBI report for the financial year 201920, the instances of bank frauds had increased almost 2 fold for the year ending in June 2020, with a combined worth of almost 1.85 Lakh Crores.
This monumental number stands owing to the fact that the technology that is available to banks and customers is equally accessible for cybercriminals and fraudsters alike. With the global connectivity of the financial industry, there is also an increase in the complexity of tracking fraud, which means that fintech firms now need to come up with solutions using advanced technologies such as AI, RPA, etc.
Ushering in an era of digital risk transformation is the need of the hour - initiatives such as rapid limit setting across portfolios, automated early warning and collection systems, compliance controls, etc. can be utilised effectively for organisations to achieve agility and focus on high-impact areas in a modular manner, thus edging towards a transformation rapidly. The same building blocks of digital transformation that are being harnessed by banks, can be used for creating a successful digital risk mitigation program.
Let us take a look at the seven building blocks, that can help banks in harnessing unique opportunities and risk mitigation:
Improved quality of data with the help of enhanced data governance and operating models, can aid in achieving better consistency in the risk in business decisions, along with ensuring responsiveness to the data needs of risk. An important enhancement that needs to be considered, is the identification of data risk as a key element of the risk taxonomy, which is linked to a specific risk appetite statement and data control framework. Apart from this, the accommodation of varieties of data is something that risk must prepare for.
Process and Workflow Automation:
Incorporation of a multitude of Automation of tasks such as collateral data entry, through RPA in smart workflows by risk, can lead to an integrated sequence that is performed by humans and machines across its journey. Apart from creating greater efficiency, smart workflows can offer customers a more seamless and timely experience.
Advanced Analytics and Decision Automation:
Advanced risk models, that are based on sophisticated technology such as ML, can identify complex patterns (for example identification of transaction sets that can indicate invoice fraud) and thus make accurate predictions of default or any other risk events. Leveraging data associated with digital footprint during customer screening, balance sheets and tax statements analysers for SMEs and Corporates etc. can all be done efficiently with the help of automated tools.
Cohesive, Timely, and Flexible Infrastructure:
Talking about the risk infrastructure, its evolution entails supporting several other building blocks, such as innovative data storage solutions, novel interfaces, better access to vendor ecosystem, etc. Usage of techniques such as application as a service, that has been procured from application service providers which could even be on open banking platforms will become the norm, with solutions such as "No Code" and "Low Code" relinquishing further control to risk executives and reducing the number of end-user computing tools.
Another very relevant example on the Infrastructure side is the Consent Architecture that the Reserve Bank of India (RBI) has put together along with iSPIRT and the Industry body Sahamati. This will reduce risk of fraudulent data practises in Lending by going to source of truth through an authorized and legal channel.
Smart Visualization and Interfaces:
With the help of risk dashboards, augmented reality platforms for customers and other such interfaces, risk will be able to deliver insights in a much more intuitive, interactive and personalised manner.
Partnering with external providers, risk can enhance customer onboarding, credit underwriting, fraud detection, regulatory reporting, etc. with the aim of acting as an enabler as opposed to a disruptor.
There are many reliable data points in the Indian Ecosystem from Aadhar to PAN to Account Aggregator to GST data. Such data available through officially ratified channels in a real time reduce possibility of Frauds and thus Ecosystem is of prime importance in Fraud Management in Lending.
Talent and Culture:
Operating within an agile culture that values innovation and experimentation, risk can accumulate a much greater share of digital-savvy personnel who are fluent in the language of both risk and business. This new talent is going to be quite critical in a digitised disc function, that includes data scientists and modelling experts. According to leaders, teams will need to inculcate the skills as opposed to higher non-risk professionals who will be then expected to learn risk.
While complex and potentially confusing, digital risk transformations are not impossible and more and more banks are now opting for them. Including within it all the tasks that are associated with digitisation efforts such as prioritisation of certain high-ROI and time-bound initiatives, alignment amongst top-executives etc. is extremely critical. Be it automated bank statement analysers during KYC, using multi-factor authentication and biometrics or advanced transaction monitoring etc. particular care must be taken when it comes to fraud detection and mitigation as it is a continuous process and the non compliance of it comes at a very heavy cost.
With the evolution of technologies, financial products that utilise the technologies, are expected to improve as well, thus becoming much more efficient for the financial industry in building fraud-free safe modules in the digital lending space.