Fighting Covid Challenges with Technology & Innovation: Pinkesh Ambavat, CIO, CRIF HighMark
CRIF’s major emphasis is on innovation. To meet the challenges created by Covid, we have come up with various innovative solutions by leveraging modern technologies such as artificial intelligence and machine learning (AI/ML), Big Data, cloud infrastructure and cloud analytics. We’re also in the midst of developing various new products particularly tapping into Analytics using AI/ML with respect to Digital Onboarding and other use cases, says Pinkesh Ambavat, Chief Information Officer, CRIF HighMark in conversation with Elets News Network (ENN).
Can you outline the latest tech trends in the financial services space in India?
In the last 5-6 years, the financial services sector in India has been on an exponential growth path. Even a conservative estimate by industry analysts points to more than 2500 FinTech companies founded in India during this timeframe.
Three key trends that will dominate Indian financial services space in the next couple of years:
(a) Digital payments: Payments as an industry have come of age, with players doubling their presence in the offline space. Digital payments have seen a significant revolution in the last few years, particularly because of the disruption brought about by e-commerce/m-commerce and online payments. Although financial inclusion is much more than just payments and transactions, payments are considered to be the gateway for financial inclusion. Consumers and businesses will continue to embrace digitized payments and we will see newer micro trends in this area in the next few months.
(b) Alternative lending: In India we have low credit penetration, compared to other global major economies. Our traditional lending banks have a rigorous documentation process which focuses more on income profile and financial history. This, more often than not, leads to non-salaried individuals, self-employed people and small business owners facing a hard time in acquiring loans. We’re seeing some technology-driven alternative lenders upending the traditional lending value chain – by engaging in both product and process innovation to improve customer experience and drive operational efficiency. Alternative lending platforms are filling this demand-supply gap in credit by targeting new customers and digitizing the credit appraisal and disbursal processes.
(c) Digital Onboarding: Digital onboarding is playing a pivotal role and its impact has been registered especially in the last few months, given the challenging situation globally. The traditional onboarding process is a very tedious one that involves huge costs and many levels of interaction with new customers. While banking customers have various options, given how all financial institutions aim for long term customer relationships, customer acquisition is becoming expensive and challenging due to sheer choice available to customers. Like I mentioned, use cases for digital payments have increased manifold in the last few months, with more and more users resorting to such instruments to ease their transactional needs. Digitizing the onboarding process enables banks and financial organizations to transform current challenges into opportunities. CRIF is already providing solutions in this space.
How is CRIF ensuring its future business success using technology in the current times?
CRIF’s major emphasis is on innovation. Led by our constant research and development efforts, we have come up with various innovative solutions, leveraging modern technologies such as artificial intelligence and machine learning (AI/ML), Big Data, cloud infrastructure and cloud analytics. We’re also in the midst of developing various new products particularly tapping into Analytics using AI/ML w.r.t. Digital Onboarding and other use cases.
How significant is your cloud partnership with Oracle?
We evaluated all the major cloud providers, and eventually realized that Oracle Cloud Infrastructure (OCI) was the perfect solution to our business needs. Oracle is more than just a technology provider for us. They are a trusted advisor and partner that can help businesses transition their complete IT stack to the cloud. Oracle is one of the best technology partners who provide a complete, integrated stack for the cloud – and they will deliver cloud services on your (i.e. a customer’s) terms.
What kind of business benefits have you started seeing from this partnership with Oracle?
The challenge for us was to keep up the high performance while being able to analyse a growing volume and variety of data – and we realized early on that traditional methods can’t keep up with this demand. We also wanted to scale-up/down our infrastructure during the festive season and as per our needs. Our primary cloud location and disaster recovery(DR) configuration were out of sync, we just didn’t have the DR capacity in case of an unforeseen disaster. Overall, we wanted to optimize costs to improve our bottom-line revenue.
We support over 5000 financial institutions, aiding millions of lending decisions every month. To accelerate innovation, improve agility and better support the business, we were looking for a robust, modern, enterprise-grade cloud solution that guarantees high performance and superior security. We chose Oracle Cloud Infrastructure to run our production workloads, and for our disaster recovery requirements. We expect to gain at least 3X improvement in performance vis-à-vis our current IT setup, along with 30% additional cost savings in the next 3 years. In summary, Oracle Cloud Infrastructure has provided us immense benefits.
Please share some details of CRIF’s digital transformation roadmap.
In addition to leveraging AI/ML, Big Data Analytics and cloud infrastructure, we’re also looking to use RPA.
Let me share with you an example of how we implemented machine learning as part of our digital transformation journey. The challenge for us was to analyse the growing volume and variety of data, given traditional analytics methods couldn’t keep up with this demand. To uncover new insights, we needed new ways of looking at the data. Models that use machine learning can provide the detail, and deep understanding needed to improve credit decisions. Models for assessing the risks that are based on machine learning offer several advantages over those that use human judgment or traditional statistical models. One of the main benefits of such ML-based models is their ability to run algorithms across large volumes of data to predict an outcome. But this is not enough to produce insights. The value of machine learning models lie in their relative lack of limitations. Commercial credit scoring algorithms will use “machine learning” technology to integrate real-time data trends and human decision making. Credit scoring will to a much greater extent be correlated with handling a big amount of data and, more importantly, how to utilize this data. The ability to squeeze every ounce of information from different sources and using new and improved algorithms is a huge challenge. A challenge that requires knowledge of what kind of data is collected. This is a key focus area at CRIF as we look to achieve our digital objectives.
Robotic process automation (RPA) is another area where we are focusing on to automate and standardize repeatable business processes. To achieve high efficiency in business operations, one needs to eliminate the risk of errors. RPA tends to introduce flexibility to business operations. RPA programs are installed on servers to combat this challenge, making processes flexible and scalable in case the demand shoots up or the scope of a process expands. In our credit bureau, product support teams handle thousands of requests daily, which include tasks like the creation of users, deactivation of users, help users on various requests, transfer files from one server to another etc. All these requests were earlier handled by the support team – which was very repetitive and a manual process. Using RPA, we have automated these requests, and human errors have been significantly reduced. This in turn helped the team to focus on the innovative and challenging tasks instead of getting caught up with just repetitive work. In effect, in addition to improving the operational agility and capacity of handling requests, we’ve also reduced operational costs to a great extent.
Implementation of bigdata is yet another important milestone that we have achieved as part of our digital strategy. Our bureau has become more operations and technology driven. The success of products weintroduce to the market depends on how quickly they can make life easy for a risk manager or an underwriter. From less inquiry volumes in 2010, we’ve seen tremendous growth over the last 10 years. This phenomenal growth has been synonymous with infrastructure growth, better SLAs and uptime all thanks to technology. Currently we have more than a billion data points. Handling this large sum of data is big challenge. We implemented BigData and AI/ML, and this has resulted in cost optimization and performance improvement on a large scale.
Source: Publication: BFSI , 17th August 2020