Over the year, evolution in global technologies has made people move forward from fixed phones to mobile phones. Today, every sector is readily and rapidly adapting the Artificial Intelligence (AI). With the fast-paced modern industries, AI is becoming an integral part of business operations. AI is not limited trend-based forecasting in marketing but its presence is getting indispensable in every vertical of the company. AI is much more efficient in analysing data patterns, based on these patterns companies acquire in-depth knowledge about their potential customers, their requirements and their behaviour.
UBank is embracing the industry shift to open data banking through a partnership with Australian fintech, Basiq, leveraging machine learning (ML) to give customers a more complete picture of their finances. The partnership hopes to offer customers predictions of future spending behaviours just by using their UBank app by 2020. This follows UBank launching its third artificial intelligence-based customer assistance offering earlier in the year, and what it claimed was the first digital human home loan application assistant. Dubbed'Mia', short for my interactive agent, the offering is built on digital human technology created by New Zealand company, FaceMe. It taps into IBM's Watson AI engine and designed to help consumers answer real-time questions during the home loan application process.
An overwhelming majority of financial firms' risk managers don't believe they can adequately assess the risks of disruptive technologies but are open to new strategies and tools to better manage emerging threats, according to a new report by Accenture. The report, 'Accenture 2019 Global Risk Management Study', is based on a survey of nearly 700 risk management executives in the banking, insurance and capital markets sectors globally. The survey found that only 11 percent of risk managers describe themselves as fully capable of assessing the risks associated with adopting artificial intelligence (AI) across their organizations, and even fewer said they are fully capable of assessing the risks associated with robotic process automation (RPA) or blockchain (9 percent and 5 percent, respectively). The report notes that the external risk environment is becoming increasingly complex, with risk teams realizing they must adapt their approaches to contend with new threats and the heightened pace of change. For instance, nearly three-fourths (72 percent) of respondents said that complex, interconnected new risks are emerging more rapidly than ever before.
In Korea, Kyobo Life has announced the launch of its new AI-based underwriting platform called Best Analysis and Rapid Outcome (BARO). The platform employs machine learning technology with the ability to process large amounts of natural language data. Kyobo life's AI-based underwriting platform employs machine learning technology and has the ability to process large amounts of natural language data The platform provides real-time services to sales staff and customers. The platform leverages Kyobo Life's underwriting manual to facilitate online deliveries by enabling instant communication with its sales staff. BARO's intelligence allows for easy approval or denial of insurance contracts with the help of screening criteria for pre-existing conditions and medical history.
Data science companies are increasingly looking at portfolios when making hiring decisions. One of the reasons for this is that a portfolio is the best way to judge someone's real-world skills. The good news for you is that a portfolio is entirely within your control. If you put some work in, you can make a great portfolio that companies are impressed by. The first step in making a high-quality portfolio is to know what skills to demonstrate.
Automation has become the latest industry catchword, but what does this mean? How can automation streamline your loan initiation process, increase the productivity of your lending officers and make your consumers happier? Manual workflows slow and error-prone, which can end with unhappy customers. Normally, the processing of a loan application involves the below steps which need to be performed by a Loan Officer to make a decision or to complete the loan request.
In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model. Moreover, little research has compared the relative performance of well-known BPM's on public datasets addressing the class imbalance problem. In this work, we apply eight classes of well-known BPMs, as suggested by a review of decades of literature, on a new public dataset named Freddie Mac Single-Family Loan-Level Dataset with resampling (i.e., adding synthetic minority samples) of the minority class to tackle class imbalance. Additionally, we apply some recent AI techniques (e.g., tree-based ensemble techniques) that demonstrate potentially better results on models trained with resampled data. In addition, from the analysis of 19 years (1999-2017) of data, we discover that models behave differently when presented with sudden changes in the economy (e.g., a global financial crisis) resulting in abrupt fluctuations in the national default rate. In summary, this study should aid practitioners/researchers in determining the appropriate model with respect to data that contains a class imbalance and various economic stages.
In partnership with a commercial bank in the Dominican Republic, the researchers conducted two separate analyses of 20,000 low-income individuals, half of them women. In the first analysis, the researchers used the individuals' loan repayment histories and gender to train a single machine-learning model for predicting creditworthiness. In the second analysis, the researchers trained a model with only the loan repayment data from the women. They found that 93% of women got more credit in this model than in the one where men and women were mixed together.
Recent studies on fairness in automated decision making systems have both investigated the potential future impact of these decisions on the population at large, and emphasized that imposing ''typical'' fairness constraints such as demographic parity or equality of opportunity does not guarantee a benefit to disadvantaged groups. However, these previous studies have focused on either simple one-step cost/benefit criteria, or on discrete underlying state spaces. In this work, we first propose a natural continuous representation of population state, governed by the Beta distribution, using a loan granting setting as a running example. Next, we apply a model of population dynamics under lending decisions, and show that when conditional payback probabilities are estimated correctly 1) ``optimal'' behavior by lenders can lead to ''Matthew Effect'' bifurcations (i.e., ''the rich get richer and the poor get poorer''), but that 2) many common fairness constraints on the allowable policies cause groups to converge to the same equilibrium point. Last, we contrast our results in the case of misspecified conditional probability estimates with prior work, and show that for this model, different levels of group misestimation guarantees that even fair policies lead to bifurcations. We illustrate some of the modeling conclusions on real data from credit scoring.
Financial institutions have a wealth of information available to them from consumers. Due to manual and antiquated models, residential lending processes so far have had several negative experiences for both the lender and the borrower. Banks are plagued with application limitations, transaction complexities and data collection and processing challenges. The'one-size-fits-all' loan application simply does not work anymore. The newly implemented and redesigned URLA (Uniform Residential Loan Application), aims to simplify, organize and streamline the entire consumer journey – from loan request, to the underwriting and approval process.