New technologies drive cost/income breakthroughs
New technologies like machine learning, biometry and block chain have entered the financial industry and will impact the banking battle on cost/income big time! The results of the Finextra/AdviceRobo survey produced this summer on Robo-Advice show that there
is an increasing awareness of the potential for unstructured data to supplement credit risk scoring and ongoing
risk modelling in consumer and SME lending. The opportunity ranges from 2% - 25% cost reductions!
Most banks have started to show interest but unfortunately, most of them also lack the vision and competences to build effective use cases on these new technologies. This is a missed opportunity as it keeps big data and machine learning in an infant testing
phase. The big opportunity however is in applying this new technology in operational processes that support big cost/income reductions. As an international FinTech entrepreneur, academic researcher of robo-advice and non-exec banker, I’ve learnt that the battle
in banking towards 2020 will be fought on cost income ratio’s. Low costs and high personalized services! With this blog I want to stimulate the wider adoption of machine learning as a strategic weapon in this cost / income battle for risk management.
Increasingly higher market risks & new regulation urge lenders to act now
More than ever financial service providers are faced with new, unexpected and possibly high risks.
Mark Carney, the Bank of England Governor told an audience in Nottingham recently that he is willing to tolerate an inflation overshoot above the Bank’s 2% target. In addition, the
Financial Times also calculated that the British government may have to pay up to £18 billion to the EU to exit the EU.
This will inevitably lead to
increased costs for the British population. With 39% of Britons already living on the edge of their mortgage payments this will lead to a higher financial risk for families. Families may be looking at
£ 2000 extra expenses a month.
Financial service providers are therefore fundamentally challenged to search for far improved ways to predict and manage risk in their portfolio across a wider set of structured and unstructured input data. Moreover, new IFRS-9 accountancy standards coming
up in January 2018 will oblige lenders to design forward looking individual impairment models rather than assess portfolio risks on historical performance. IFRS-9 will in 2017 and 2018 force the highly regulated financial risk world to switch from traditional
logistic regression models working on historical structured data that are super static. The shift will be towards self-learning technologies like random forest, neural network and deep learning driven risk scoring working on both structured and unstructured
data. As we are all data-agents,
99% of the data currently available in the world has been produced over the past two years. Improved risk management applies new data groups among them that differentiate in risk probabilities. This is where cost/income breakthroughs can be found. Depending
on the asset class a lender is operational in, loan losses account for 2% – 25% of the total costs.
Cost/ Income Breakthroughs in risk management: Off the shelf arms or do it yourselves?
New big data FinTech solutions might provide some support in this highly pressured risk landscape. Artificial Intelligence company AdviceRobo offers big data and machine learning driven early risk warnings with Gini-scores up to 95%! The British company
Credit Kudos applies aggregated transaction data for machine learning driven risk scoring and Aire applies customer application data for scoring creditworthiness of prospects. The data explosion and the use of new technologies such as Machine Learning help
to reduce loan losses at the granular level and at an individual customer level. This will highly support responsible lending, but also drive cost/income breakthroughs of 2% - 25% by offering advanced risk scoring. Developing machine learning competences therefore
are a super impactful strategic weapon for banks who want to excel in responsible lending and win the battle in the lower cost/income lending markets.