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Our participation at Portfolio's Lending conference is never a question. This year, Gábor Győrfi represented Loxon and talked about the revolution in the field of lending. You can read a short summary of the presentation from Portfolio below.

Gábor Győrfi, the regional sales director of Loxon held a speech about the areas of application of artificial intelligence in the field of lending. This year and the last banks have spent the most on digital rejuvenation, both in the case of developing back end or front end processes.
What follows this is the structuring and the analisation of data: each company sits on untouched 'goldmines' of information.
Analysing these conglomerates of data makes it possible to carry out prompt profiling of each client. The automation of notifications, offers, ordering services becomes possible, they can then personalise services and the bank can reduce their risks.

(Source: Portfolio.hu, https://goo.gl/dwDhma )

At the end of March the Middle East Retail Banking conference was put on by Fleming for the 13th time. Around 120 guests attended the event this year housed by Habtoor Grand Resort Dubai. Tamás Erni the chief executive president of Loxon, talked about innovative risk management solutions then presented an exciting case study.

 

Published by Andrea Cavina | Co-Founder @evolynx

Today, just few hours ahead of the LendingUP! conference in Prague, we managed to hear from our speaker Gábor Győrfi. Gabor, thanks first of all for this interview! In the last few years we've seen an increasing activity in the lending market and the rise of many non-bank lenders: for banks to stay in the game it's paramount to broaden sources of information about potential clients, as well as to improve and automate decision and client monitoring practices. What are the challenges when it comes to the integration of external and internal data?

Nowadays we are all flooded with data. Broadening the sources of information is easy, one can find dozens and dozens of new sources. The real challenge is not this. The real challenge is for the banks to find the proper business use case, the proper business goal, where they want to improve (like faster customer service, more lucrative product offering, proactive action to customer needs, etc) and then find innovative techniques, approaches, new data sources to understand the way to reaching this business goal. The second largest challenge is linking internal and external databases so that one can use the combined information, like finding and identifying our customers in an external database and interconnected to that is the question how we can get the consent of the customer for using his/her data available in external sources.

Let's now talk about the other big player in the game: Banks. They should provide loan applicants with a seamless digital journey while cutting operational costs: what processes should they focus on to shorten the progress time of loan origination to a minimum level and what are the possibilities offered by technology?

Newest technologies provide the opportunity to lenders to reduce the loan application process to seconds, technology supports this basically in every step of the process. Through getting credit applications online, through mobile channel and having the lending systems integrated with external databases supporting fully automated verification, through automated credit scoring based on internal and external data (like social scoring, advanced behavioural scoring), or fully automated KYC, to the point of digitally identified customers (e.g. through video chat) and digitally verified customer signature on contracts technology now enables banks to have fully data driven lending processes.

I see. It's also interesting thinking about how lenders face the challenge of achieving an effective credit rating and credit scoring. It can be summarised by a simple question: who should be provided with a loan? What are the latest approaches in credit scoring?

The latest approaches in credit scoring are focusing on customer behaviour not analysed before. We apply social scoring based on social media behaviour and relations of our clients/potential clients, we apply psycho scoring based on customer behaviour, and we combine all these with the financial transactional behaviour, web browsing behaviour of the customer to get a much more accurate profile of the customer as with only traditional techniques.

Thanks. We know that the use of machine-learning techniques and Big Data analytics can help banks improve the predictability of credit early-warning systems and offers substantial upside in portfolio monitoring, contributing to a significant reduction in defaults and enhancing the overall lending performance. Would you tell us more?

The newest technologies made the horizon of early warning much brighter than before. Early warning is basically, as its name says it about finding out potential future problems with today's healthy customers as early as possible. Today the technology allows us to run our early warning methods daily, or even intraday on the customer portfolio to find triggers, patterns leading to healthy customers becoming problematic. One example is to find triggers for predicting customer skip, where traditional methods that were run on a monthly, weekly basis were too late. Now with the help of daily data loads, extended databases providing us insight to the behavioural changes of our customers, early warning triggers inform us immediately of such patters, behaviour changes and the prescriptive algorithms immediately start the action plans.

We organised LendingUP! thinking about a geographically diversified audience, with delegates coming from all over Europe: how important is this for you in relationship with scaling into new markets?

For Loxon still around 50% of our business comes from Europe, so Europe is a key market to us. We are convinced LendingUP! can support us in reaching out to new customers, network with our existing ones, and show to them an insight into how Loxon uses the newest technologies to provide them business added values throughout their lending process and credit management.

Sounds like a good fit then! So this is the end of this short interview, thank you again for your time, Gabor. Few hours and we'll listen live to your presentation. Looking forward to meeting you.

https://goo.gl/kPPBYB