Kuliza comes with a great understanding of Digital Lending. They have helped us achieve faster time to market and agility that is critical to compete in the current reign of digital disruption. I strongly recommend Kuliza to companies looking to achieve edge through Fintech innovation
Senior Vice President and Head of Digital Finance - Aditya Birla Finance Limited
Lend.In platform enabled process automation and operational efficiency using API based technologies. This helped us to go to market quickly and enhance our product based on customer and market needs. The platform helped us blend in with new technologies and made us future-ready for the upcoming technologies in the future
Chief Marketing Officer - FE Credit
Digital wallet based consumer micro-lending to drive financial inclusion
Taking advantage of the digital wallet landscape in India, a leading lending enterprise simplified the lending process with a digital wallet ecosystem partner. The lending institution leveraged pre-existing data and spending patterns from the digital wallet company to recommend personalized loan offerings to customers. Lend.In’s Credit Decisioning Engine fetched the detailed credit information of the customers, based on which credit assessment was conducted and the loan offer was generated and passed on to the digital wallet platform. Lend.In helped the lending institution scale to handle thousands of loan applications simultaneously and accelerated the time to market by 2x.
Data migration from On-Premise to Public Cloud
To significantly reduce cost and operational risk, a leading credit institution in Vietnam migrated all the data to a secure environment and scaled its operations. Lend.In’s cloud ready platform provided the financial institution with a competitive edge by moving their business applications and data to a public cloud platform. The complexities in cloud migration depends on the size of business operations of lending enterprises, Lend.In employed a successful cloud migration approach to ensure that data-dependent applications faced minimal disruption and a smooth transition.
Digital Education Loan approval using unconventional data
A leading NBFC specializing in education loans wanted to improve its decision making process and ease student loans by considering parameters relevant to students without any credit history. The NBFC enabled a straight-through-processing of education loans which transitioned seamlessly from lead capturing to in-principal approval. Lend.In through the use its Credit Decisioning Engines enabled consideration of multiple parameters including standardised test scores and the tier of university to establish a score provided for the generation of rate of interest. By using external API integrations and back-office portals for verification, Lend.In enabled a significant reduction in TAT.
Enabling consumer finance services to unbanked population in Southeast Asia
To further it’s reach across all demographics, a new age NBFC desired to employ a system flexible to launch a loan product which was new to the market. Lend.In helped the NBFC implement easy and convenient consumer finance services available in all localities nationwide. The payday loan product warranted the need for a system that enabled faster verifications and decision making which led to Lend.In creating an STP loan journey for these small sized loans. Leveraging the Credit Decisioning Engine and the API Integration Broker, eKYC and interest calculations were made easier leading to a significant reduction in TAT.
Transforming Customer Onboarding eKYC Through Case Management Models
Know Your Customer (KYC) caused banks and credit institutions to spend a tremendous amount of time and efforts in order to guarantee that they act in accordance with the ever-evolving laws and regulations imposed on them. Lend.In’s unique Case Management Model helped manage the copious amounts of customer information, for a lending enterprise provided during the application process and moved each prospect that entered the loan cycle, in a particular case. These cases were initiated when certain conditions like unusual out of area activities, outdated account records, adverse external mentions or businesses that partnered with organisations that are off the radar, were triggered. Lend.In provided the assurity of compliance across geographies and also enabled the efficient use of third party integrations to streamline the KYC process.
Bancassurance: Diversifying Customer Portfolio & providing another layer of security to customers
A leading financial enterprise and a few popular insurance companies wanted to leverage their respective strengths to increase outreach and profitability. The vast reach of banks with a strong customer base and insurance companies bringing in specialized knowledge of the insurance market led to the bancassurance model. Enabled by Lend.In’s digital processes and analytics, the essential components of superior customer experience, and omnichannel engagement shaped the formula for sustained bancassurance growth for the financial institution and insurance companies alike. Mounds of data that financial enterprises sit on such as demographic and financial details of clients, their transactional details, their complete portfolio, and credit repayment history, were all used in the creation of the model. Lend.In furnished a risk-free income to the financial enterprise that utilized this data to predict customer needs, deliver customized products and diversify their customers’ portfolio.
360-degree Customer view that Harnesses Real-Time Data
To provide a seamless experience to their customers as well as bank personnel, a data driven NBFC sought out to find an efficient solution. Lend.In though the use of Case Management framework provided back-office portals, the first of its kind, which enabled a faster case transfer and allowed key stakeholders to view each and every step within an onboarding customer’s loan journey. Through the lending portal, relationship managers assigned to each applicant could fill the application form on behalf of the applicant if the applicant was unable to midway. This drastically reduced the overall TAT in non-STP journeys and increased the efficiency of manual intervention processes.
End-to-end digital auto financing platform for Southeast Asia market
The need to reduce the overall TAT and maximise the existing core banking system was a major challenge for a leading NBFC. Lend.In devised a complete straight-through-process model specifically created for an auto loan, in order to create a paperless loan journey with higher customer-oriented functionality and a faster time-to-market. This was achieved by exposing data within the core banking system through Lend.In’s API Integration Broker, expediting the realisation of an interconnected and interdependent ecosystem. The final result was a seamless straight through process auto loan journey from in-principle approval to loan disbursement.
Cluster based lending for Small and medium enterprises
An impact focused credit institution realized the benefits of a cluster-based approach to lending and wanted to establish an innovative SME lending solution for each of the lending products in its portfolio. Using Lend.In’s low-code platform, the credit institution built a digital platform that enabled simple eligibility checks and KYC processes. Built-in lead management system and workflow enabled online forms empowered sales agents to quickly onboard customers and also manage their life cycle. Loan applications could instantly be approved or rejected in real-time based on the documents provided by the customer and uploaded onto the system or post a rudimentary credit analysis.
Customer journey configuration in real-time to amplify user experience
Investing in user interfaces and experiences became a turning point in improving customer onboarding. A leading lending institution configured UI components in real time without the use of any code. Lend.In’s Digital Experience Manager (DEM) along with the Product Configuration Platform created custom journeys for different groups and segments. The drag and drop UI components reduced the time taken by business users to configure the front end of the applications by nearly 5-7 times faster than custom development. To statistically measure the impact of the configured UI, it allowed for A/B testing of journeys which helped the enterprise to learn faster and replicated changes on one platform to other web applications, partner sites and mobile applications in real-time.
Behavioural Decision Making for a lending marketplace using 3rd party e-commerce websites
An impact based NBFC leveraged Lend.In to design and build an innovative digital lending platform that aimed to empower small business owners by offering them quick access to capital. During the process, the lending institution faced challenges in improving their decision-making capabilities to better gauge entrepreneurs. Through employing analytics-powered engines they were able to evaluate the entrepreneur better and make capital allocation decisions. Based on the information provided by the entrepreneur, Lend.In provided functionalities to analyse customer bank statements and performance history in association with their behaviour across e-commerce platforms. Sentiment analysis provided the investor a personality perspective to the entrepreneur. The platform provided a layer of trust and bridged the gap between the investor and potential entrepreneur before investing in their venture.
Enhanced decision-making capabilities for lending platform by providing IoT based insights
An impact driven NBFC sought out to improve their decision making capabilities by leveraging data available across all industry sources. The method to address this problem was to utilize IOT in their decisioning process. Lend.In’s Low-Code system captured and analysed data from smart devices and IoT was implemented within the system through exposed APIs. A dedicated thread was open for APIs in which the data was stored and visually represented. The entire implementation was aimed at improving decision making capabilities of the underwriter where the consumption and usage of entities were analysed to predict the behavioural patterns and rationale.
Robotic lending mobile application to provide loans under 15 minutes
A technology driven NBFC in Vietnam strived to deliver unique financial products to customers and desired to use a blend of Design Thinking, Robotic Process Automation & Digital Process Automation in its implementation. Lend.In helped build a comprehensive consumer finance process on its low-code platform which leveraged AI-based character, facial recognition tools and automated risk assessment and scoring. Through this, Lend.In minimised fraudulent activities and reduced turn-around-time of the loan journey to under 15 minutes that allowed instant approvals and immediate disbursements, appealing to the consumer market in Vietnam.
Increasing Customer Lifetime through Pre-Approved Loans
The lending enterprise was on a mission to strengthen their relationships with customers and increase their lifetime value. It leveraged analytics and intelligence to identify existing customers and offered pre-approved loans based on risk profiling and past transactions data. Lend.In achieved this by leveraging pre-existing data available with the organization to derive patterns and insights which could be displayed on the key stakeholders’ dashboards. The simple yet efficient pre-approved loan journey was configured using the low-code platform which made loan disbursement easier and efficient. The lending enterprise helped the customers avail loans is a matter of clicks and increased their brand loyalty.
Credit Assessment powered by Machine Learning
Data is the new oil and it fuels financial institutions to enable smarter credit assessment. One of the leading non-banking financial institutions realised this opportunity and desired to move away from manual underwriting to automation for more efficiency. This was achieved by configuring various kinds of credit models that supported unconventional parameters. Lend.In’s dynamic Credit Decisioning Engine utilized machine learning algorithms to enable real-time credit assessment while simultaneously helping lenders create high-performing predictive models, using customers’ credit history and the power of big data. Through the usage of ML, the loan processing was improved, case rejections were reduced and overall burdens on the credit and underwriting teams were reduced by providing real time recommendations
Cutting down in-principle approval to under 15 minutes for Business Loans
To deliver a full spectrum of digital capabilities and reduce customer drop offs, a financial institution in Southeast Asia transformed its business loans process by enabling smarter business KYC process without involving copious number of fields and details from borrower, conducted business verification by fetching data from third parties integrated in the system and enabled real-time credit decisioning using Lend.In’s Credit Decisioning Engine. This substantially improved the decisioning and risk profiling in a swift manner. Lend.In’s low code platform offered the flexibility to configure, deploy and test credit models while reducing the policy changed TAT to less than 1 day and in-principle approval to under 15min.
Synergistic partnership to drive financial inclusion of the unbanked Vietnamese population
To Improve financial inclusion and serving millions of the unbanked population of Vietnam, a credit institution in Vietnam partnering with Asia’s first and only metasearch engine. Through this partnership, the credit institution established a broader audience base while extending its organic loan portfolio. Lend.In’s API Integration Broker enabled smooth integration of the features of the robo-lending application to the metasearch engine platform. Lend.In ensured the quick launch of a new widget-based app within a week, saving the time and effort.