Norwegian Bank Research Articles

Mortgage lending valuation bias under housing price changes and loan-to-value regulations

We examine valuation bias, regulatory loan-to-value limits, and real estate price fluctuations using unique data from two Norwegian banks. We investigate whether automated valuation methods significantly affect foreclosure discounts. Banks using multiple valuation methods tend to report loan-to-value bias by selecting the most optimistic value, underestimating and underreporting risk in declining property markets. This risk increases near regulatory loan-to-value limits, and the findings are robust across homogenous and heterogeneous properties. We recommend reporting automated valuation estimates for all applicable properties and disclosing the percentage of valuations done with different methods to improve transparency and risk comparability between banks.

The viral trajectory of a management idea: A longitudinal case study of the Balanced Scorecard in a Norwegian bank

The viral trajectory of a management idea: A longitudinal case study of the Balanced Scorecard in a Norwegian bank

Changes in credit score, transaction volume, customer characteristics, and the probability of detecting suspicious transactions

PurposeThis study aims to use a unique customer-information data set from a Norwegian bank to identify how small changes in firm-specific factors correlate with the risk of a client subsequently being involved in suspicious transactions. It provides insight into the importance of updating client risk based on changes in transaction volume and credit risk to enable effective resource use in transaction monitoring.Design/methodology/approachChanges in a firm’s bank use and accounting data were tested against subsequent flagged and reported customers to identify which changes led to a significant increase in the probability of engaging in a transaction identified as suspicious. Prioritizing resources to firms that remain suspicious after further controls can improve the risk-based approach and prioritize detection efforts. The main factors were customer probability of default (credit score), size and changes in customer characteristics. The cross-sectional data set contained administrative data on 8,538 corporate customers (219 with suspicious transactions that were subsequently flagged, 64 of which were reported). A binomial logit model was used.FindingsChanges in transaction volume and bank use are significant in predicting subsequent suspicious transactions. Customer credit score changes were significantly positively correlated with the likelihood of flagging and reporting. Change is a stronger indicator of suspicious transactions than the level. Thus, frequent updating of client risk and using a scale rather than risk categories can improve client risk monitoring. The results also showed that the current anti-money laundering (AML) system is size-dependent; the greater the change in customer size, the greater the probability of the firm subsequently engaging in a suspicious transaction.Research limitations/implicationsClient risk classification, monitoring changes in a client’s use of the bank and business risk should receive more attention.Practical implicationsThe authors demonstrate that client risk classifications should be dynamic and sensitive to even small changes, including monitoring the client’s credit risk changes.Social implicationsDirecting AML efforts to clients with characteristics indicating risk and monitoring changes in factors contributing to risk can increase efficiency in detecting money laundering.Originality/valueTo the best of the authors’ knowledge, this is the first study to focus on changes in a firm's use of a bank and link this to the probability of detecting a suspicious transaction.

The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank

Banks generally use credit scoring models to assess the creditworthiness of customers when they apply for loans or credit. These models perform significantly worse when used on potential new customers than existing customers, due to the lack of financial behavioral data for new bank customers. Access to such data could therefore increase banks’ profitability when recruiting new customers. If allowed by the customer, Open Banking APIs can provide access to balances and transactions from the past 90 days before the score date. In this study, we compare the performance of conventional application credit scoring models currently in use by a Norwegian bank with a deep learning model trained solely on transaction data available through Open Banking APIs. We evaluate the performance in terms of the AUC and Brier score and find that the models based on Open Banking data alone are surprisingly effective in predicting default compared to the conventional credit scoring models. Furthermore, an ensemble model trained on both traditional credit scoring data and features extracted from the deep learning model further outperforms the conventional application credit scoring model for new customers and narrows the performance gap between application credit scoring models for existing and new customers. Therefore, we argue that banks can increase their profitability by utilizing data available through Open Banking APIs when recruiting new customers.

Explainable AI for Credit Assessment in Banks

Banks’ credit scoring models are required by financial authorities to be explainable. This paper proposes an explainable artificial intelligence (XAI) model for predicting credit default on a unique dataset of unsecured consumer loans provided by a Norwegian bank. We combined a LightGBM model with SHAP, which enables the interpretation of explanatory variables affecting the predictions. The LightGBM model clearly outperforms the bank’s actual credit scoring model (Logistic Regression). We found that the most important explanatory variables for predicting default in the LightGBM model are the volatility of utilized credit balance, remaining credit in percentage of total credit and the duration of the customer relationship. Our main contribution is the implementation of XAI methods in banking, exploring how these methods can be applied to improve the interpretability and reliability of state-of-the-art AI models. We also suggest a method for analyzing the potential economic value of an improved credit scoring model.

Bank Consolidation, Interest Rates, and Risk: A Post-Merger Analysis Based on Loan-Level Data from the Corporate Sector*

ABSTRACT In this paper, we analyze the bank merger between DnB and Gjensidige Bank in 2003, ranked by market share as number one and number three in the Norwegian bank market. Focusing on loans to firms, our difference-in-differences analysis shows no increase of concentration of new loans. The concentration in affected markets (markets where both merging parties were present) developed similar to unaffected markets. Moreover, the interest rate tended to be lower in the affected markets relative to unaffected markets, but this relationship is weak and not statistically significant. The merger also affected the riskiness of loans only marginally. These weak effects could be the result of efficiency gains in the form of lower costs being pass-through to customers, and the increased market power (and consequently higher interest rates) canceled each other out. The remedial measures imposed by the Norwegian Competition Authority on the two merging parties are also likely to explain some of the modest effects of the merger. The weak effects are largely coincident with international literature showing the effects of mergers and acquisitions in the banking sector to be modest.

An end-to-end deep learning approach to credit scoring using CNN C XGBoost on transaction data

The performance of credit scoring models is closely linked to a bank’s profitability. Application scoring models for potential customers usually perform worse than models for existing customers. This is due to the lender not having access to the financial behavioral data of potential customers. Access to such data about potential customers could therefore increase a bank’s profitability. Open banking application programming interfaces (APIs) provide access to 90 days of historical data on potential customers’ balances and transactions. We examine the performance of credit scoring models developed using such data from a Norwegian bank. We find that traditional regression models perform poorly, while machine learning (ML) methods can provide models that perform satisfactorily based on these data alone. Further, we find that the best performing models are based on an end-to-end deep learning approach, where machine learning algorithms create explanatory variables based on non-aggregated data. These results indicate that data available through the open banking APIs can be an important data source when banks assess the creditworthiness of potential customers. In combination with end-to-end deep learning methods they have the potential to increase the performance of a bank’s application credit scoring models and thus increase the bank’s profitability.

Too rich to learn – when action researchers work against senior management and their use of performance management

ABSTRACT We analyse and self-reflect on an action research project conducted in a financial organisation twelve years ago. The research question was: What are the challenges of initiating an action research project in a financial organisation with top-down control that uses the Balanced Scorecard? The data came from action learning seminars with line managers, observations, interviews, and meetings in a Norwegian bank (Bank) with approximately 800 employees. The data were analysed using thematic analysis. This study’s findings indicate that performance management and top-down senior management control may unintentionally hinder the possibilities for proceeding with action research projects. The researchers lacked open communication with senior management about the purpose of the project, and the managers who participated in the action learning found it difficult to explain to colleagues and senior management what they learned from the processes. Action research did not fit with the Bank’s strategic use of performance management. The project may have increased the stress on the line managers as we identified problems without providing the opportunity to make real changes in their organisation. As a result, we determined that action researchers should be cautious in initiating action research without concrete support, active participation and ‘common ground’ dialogues with senior management.

Norway

Norwegian banks and other financial institutions rely heavily on capital markets for liquidity and risk management. Liquidity conditions in the Norwegian financial sector are affected by central bank operations and the lending and funding activities of financial institutions, both domestically and abroad. Nearly 40 percent of the funding of Norwegian banks is obtained from market sources, using commercial paper, covered bonds, and senior unsecured bonds issued both domestically and abroad. Correspondingly, money markets, foreign exchange (FX) swap markets and bond markets are crucial to the credit intermediation process and a dislocation in these markets—the inability of financial institutions to roll over, or obtain new, funding—could have significant consequences for financial stability. Against this background, this note analyzes core funding markets for Norwegian banks and assesses Norges Bank’s capacity to manage systemic liquidity conditions and counteract liquidity shocks in normal times and in times of stress.

Działania Banku Centralnego Norwegii w czasie kryzysu gospodarczego 2007+

The period of global economic crisis starting from 2008 is a great challenge for public institutions responsible for macroeconomic management. Although Norway is a country that has not been significantly affected by the crisis, the pace and scale of countercyclical actions were a major challenge for Norwegian institutions. The activities of the Norwegian central bank included interest rate policy, liquidity management on the interbank market and supervision of the oil fund. The policy of interest rates had a huge importance in 2008 when the bank made a few decisions under conditions of high uncertainty and unprecedented scale. The initial phase of the crisis was also crucial in terms of liquidity and policy of enhancing stability of Norwegian banks. Due to high fluctuations of prices on stock, bond and real estate markets it was also extremely difficult to manage the oil fund portfolio. Nevertheless, through effective policy the Bank of Norway has contributed to minimizing the crisis’ costs.

Facilitating Climate-Smart Investments

The financial sector will play a key role in transitioning toward low-carbon and climate-resilient development, but financial decision makers need tailored information to guide climate-smart investment decisions. Here, we reflect upon the Sustainable Edge report and investor engagement to pilot a new approach to climate-risk assessment for companies.

Blurring the Borders. How the Central Banks of the European Neutrals During the First World War Became Part of the State Machinery: Examining the Case of Norway

This article analyses the relationship between the central banks and governments in the neutral countries during the First World War, with focus on the Norwegian development. It examines how independence was challenged, and the framework is a concept of central bank independence, which regards non-lending to the state as vital to the functioning of the central banks. This is a novel approach to the development during the war as the perspective has barely been discussed in the literature in Norway, and also seems to be disregarded in the standard international literature on central bank development. From this perspective the article argues that the Norwegian central bank’s pre-war independence was substantial compared to other central banks. Moreover, the distinct borders between central bank and government also safeguarded Norges Bank’s autonomy longer than in comparable countries after the outbreak of war. However, by the end of the war, Norges Bank had become one of the neutral central banks most interwoven with the state. Based on the historical development in different countries, the article questions the notion of the standard literature that lending to the state in a crisis was a central bank duty.

Financial Globalization and Bank Lending: The Limits of Domestic Monetary Policy?

We empirically analyze how bank lending reacts to monetary policy in the presence of global financial flows. Employing a unique and novel dataset of the funding modes and currency composition of the full population of Norwegian banks in structurally identified regressions, we show that the efficiency of the bank lending channel is affected when banks can shift to international funding and thus insulate their costs of funding from domestic monetary policy. We isolate the effect of global factors from domestic monetary policy by focusing on the deviation of exchange rates from the prediction of (uncovered and covered) interest rate parity. The Norwegian banking sector represents an ideal laboratory since the exogenous exchange rate dynamics allows for a convincing identification of the relation between lending and global factors.

Financial Globalization and Bank Lending: The Limits of Domestic Monetary Policy?

We empirically analyze how bank lending reacts to monetary policy in the presence of global financial flows. Employing a unique and novel dataset of the funding modes and currency composition of the full population of Norwegian banks in structurally identified regressions, we show that the efficiency of the bank lending channel is affected when banks can shift to international funding and thus insulate their costs of funding from domestic monetary policy. We isolate the effect of global factors from domestic monetary policy by focusing on the deviation of exchange rates from the prediction of (uncovered and covered) interest rate parity. The Norwegian banking sector represents an ideal laboratory since the exogenous exchange rate dynamics allows for a convincing identification of the relation between lending and global factors.

The Global Financial Cycle, Bank Capital Flows and Monetary Policy. Evidence from Norway

We investigate the importance of a global financial cycle for gross capital inflows based on monthly balance sheet data for Norwegian banks. The VIX index has been interpreted as an investor fear gauge and associated with a global financial cycle. This index has also been found to impact real activity. We include both a global activity variable and the VIX index in our structural VAR model of capital inflows. We find that when global activity falls, banks' foreign funding share falls. Our results suggest that global real activity rather than a global financial cycle is a main driver behind the volume of bank capital inflows. We also study domestic monetary policy and implications for capital flows. Domestic monetary policy helps absorb VIX shocks and there is no indication of procyclical (carry trade) effects on funding. Monetary policy affects activity and inflation in a standard fashion, and the exchange rate acts as a buffer when shocks hit the economy.

Circulation and diagnostics of Puumala virus in Norway: nephropatia epidemica incidence and rodent population dynamics.

Hantaviruses pose a public health concern worldwide causing haemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS). Puumala virus (PUUV) is the most prevalent hantavirus in Central and Northern Europe, and causes a mild form of HFRS, also known as nephropathia epidemica (NE). In nature, the main host of PUUV is the bank vole (Myodes glareolus), and transmission to humans occurs through inhalation of aerosols from rodent excreta. Nephropathia epidemica is particularly prevalent in Nordic countries, however, few studies of PUUV have been performed in Norway. The aim of this study was to analyse the dynamics of PUUV in Norway and compare with bank vole population dynamics, and also to complement the current diagnostic methodology of NE in Norway. Our results showed a significant seasonal and geographical variation of NE, and a general parallel peak trend between bank vole population densities and human NE incidence. A real-time and a nested PCR were successfully established as an invaluable diagnostic tool, with detection and sequencing of PUUV in a human serum sample for the first time in Norway. Phylogenetic analysis showed clustering of the obtained human sample with previous Norwegian bank vole isolates.

Bounds on total economic capital: the DNB case study

Most banks use the top-down approach to aggregate their risk types when computing total economic capital. Following this approach, marginal distributions for each risk type are first independently estimated and then merged into a joint model using a copula function. Due to lack of reliable data, banks tend to manually select the copula as well as its parameters. In this paper we assess the model risk related to the choice of a specific copula function. The aim is to compute upper and lower bounds on the total economic capital for the aggregate loss distribution of DNB, the largest Norwegian bank, and the key tool for computing these bounds is the Rearrangement Algorithm introduced in Embrechts et al. (2013). The application of this algorithm to a real situation poses a series of numerical challenges and raises a number of warnings which we illustrate and discuss.

Learning and knowledge as interrelations between CoPs and NoPs

Purpose– The purpose of the study is to investigate the characteristics and interrelations between informal communities of practice and formal networks of practice and its consequences for learning and new knowledge at work.Design/methodology/approach– A case study was conducted in a Norwegian bank using qualitative explorative studies and in-depth interviews with financial advisers and their leaders.Findings– The findings demonstrate that in the absence of integration efforts via a network of practice, multiple communities of practice to ensure knowledge flow is not enough. By the same token, it is possible for new knowledge to be accepted where a community of practice functions cohesively in a singular form in close interrelation with network of practices, but only when both communities of practice and network of practices are supported by a participatory leader.Research limitations/implications– The total of 30 in-depth interviews might call for further studies. Also, studying a Norway bank alliance does not account for cultural differences between countries.Originality/value– To the author's knowledge there are no studies that investigate the interrelations between informal communities of practice and formal network of practices.

The politics of sensemaking and sensegiving at work

Purpose– The aim of this paper is to investigate how political activities and processes influence sensemaking and sensegiving among top management, middle management and employees and to examine its consequences for implementing new knowledge.Design/methodology/approach– Data were collected in a Norwegian bank using in-depth interviews with middle managers and financial advisers. Observations of meetings, informal conversations and verbatim notes were also used in data collection among top managers. A practice-based approach was used as an analytical lens.Findings– Top managers' political activities of excluding others from the decision process affect their sensemaking and resulted in sensegiving contradictions between spoken intent and how to change practice. Middle managers' political activities were to accept top managers' sensegiving instead of managing themselves in their own sensemaking to help financial advisers with how to change their role and practice. As a result, middle managers' sensemaking affects their engagement in sensegiving. For financial advisers, the political processes of top and middle managers resulted in resistance and not making sense of how to change and implement new knowledge.Research limitations/implications– A total of 30 in-depth interviews, observations of five meetings and informal conversations might call for further studies. In addition, a Norwegian study does not account for other countries' cultural differences concerning leadership style, openness in decisions and employee autonomy.Originality/value– To the author's knowledge, no studies identify the three-way conceptual relationship between political activities, sensemaking and sensegiving. In addition, the author believes that the originality lies in investigating these relationships using a three-level hierarchy of top management, middle management and employees.

The Relation between Banks' Funding Costs, Retail Rates and Loan Volumes: An Analysis of Norwegian Bank Micro Data

We use a dynamic factor model and a detailed panel data set for six Norwegian bank groups to analyze i) how funding costs affect retail loan rates and ii) how retail rate differences between banks affect market shares. The data set consists of quarterly data for 2002Q1-2011Q3 and include information on loan volumes and retail (interest) rates for loans to firms and households. The cost of market funding is represented in our analysis by the three-month money market rate and a proxy for market risk - the credit spread on unsecured senior bonds issued by Norwegian banks. Our estimates clearly suggest incomplete pass-through: a 10 basis points increase in the market rate leads to an approximately 8 basis points increase in retail loan rates. We also find that credit demand from households is more elastic with regard to the loan rate than demand from businesses.